PETTERI MÄKELÄ LOCAL POSITIONING SYSTEMS AND INDOOR NAVIGATION

PETTERI MÄKELÄ LOCAL POSITIONING SYSTEMS AND INDOOR NAVIGATION

PETTERI MÄKELÄ

LOCAL POSITIONING SYSTEMS AND INDOOR NAVIGATION

Licentiate of Science Thesis

The subject has been approved at the

Department of Electrical Engineering council meeting on May 7, 2008

Examiners: Professor Markku Kivikoski

Professor Jarmo Takala

ABSTRACT

TAMPERE UNIVERSITY OF TECHNOLOGY

Degree Programme in Electrical Engineering

Mäkelä, Petteri: Local Positioning Systems and Indoor Navigation

Licentiate of Science Thesis, 116 pages

June 2008

Major: Electronics

Examiners: Professor Markku Kivikoski, Professor Jarmo Takala

Keywords: local positioning system, indoor navigation, WLAN, UWB, AGPS iii

The market of location-based services is anticipated to grow strongly in the near future.

Map-based guidance and navigation, intelligent transport services and tracking of people and valuable assets, are examples of typical positioning applications. Many location-based services depend on Global Positioning System (GPS) as a position data provider. However, there is also a growing interest in positioning applications which are independent from satellite-based navigation technologies. These positioning systems, which are designed to operate inside a limited geographical area, are called local positioning systems. Local positioning systems are needed in indoor navigation and in wireless sensor networks, which measure various environmental parameters.

This thesis is a literature research, which considers the most promising current and emerging local positioning technologies. The research aims to match the characteristics of the identified local positioning techniques to the needs of various location-based applications. Various types of signals, sensors, observables and computation algorithms used with these positioning technologies are presented and compared.

There is no single technology that may be relied upon in all environments to provide accurate location information. However, there clearly are a few promising technologies which outperform certain others, depending on what the purpose of usage is. The most widely used local positioning systems today are based on the use of wireless local area network (WLAN) signals. Of these systems, the system based on location fingerprinting method was found the most promising. On the other hand, technology based on signal propagation time measurements of ultra-wideband (UWB) was found the most interesting emerging local positioning technology due to the accuracy and performance of UWB in multipath conditions. Also some other local positioning technologies, such as computer vision based positioning and dead-reckoning method, were identified as suitable technologies for certain applications.

Based on this research it can be anticipated that in the future fusing indoor positioning techniques with the satellite-based navigation systems would be one of the most important research topics. Integrating a local positioning technology, such as UWB positioning, with GPS as a single location based service would open remarkable new market opportunities.

iv

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TIIVISTELMÄ

TAMPEREEN TEKNILLINEN YLIOPISTO

Elektroniikan koulutusohjelma

Mäkelä, Petteri: Paikalliset paikannusjärjestelmät ja sisätilanavigointi

Lisensiaattityö, 116 sivua

Kesäkuu 2008

Pääaine: Elektroniikka

Tarkastajat: professori Markku Kivikoski, professori Jarmo Takala

Avainsanat: paikannus, sisätilapaikannus, WLAN, UWB, AGPS

Paikannuspalveluiden markkinoiden on ennustettu kasvavan lähivuosina voimakkaasti.

Esimerkkejä paikannuspalveluista ovat erilaiset kartta- ja reittipalvelut sekä tavaroiden ja ihmisten seuraamiseen tarkoitetut sovellukset. Nykyiset paikannuspalvelut perustuvat enimmäkseen satelliittipaikannusjärjestelmien tuottamaan sijaintitietoon. Monissa sovelluksissa halutaan kuitenkin käyttää satelliiteista riippumattomia paikannusteknologioita, jotka perustuvat paikallisten verkkojen käyttöön. Paikallisiin verkkoihin perustuvia navigointiteknologioita tarvitaan erityisesti sisätiloissa tapahtuvassa paikannuksessa ja ympäristön mittaamiseen tarkoitetuissa langattomissa anturiverkoissa.

Tämä lisensiaattityö on kirjallisuustutkimus, jonka tarkoituksena on esitellä tärkeimmät paikallisiin verkkoihin perustuvat paikannusteknologiat. Työn tavoitteena on tunnistaa kunkin paikannusteknologian erityispiirteet ja etsiä tiettyyn käyttötarkoitukseen parhaiten soveltuva paikannusteknologia. Työssä käsitellään yleisimmät paikannusjärjestelmissä käytetyt signaalit, anturit, havaintosuureet sekä laskentamenetelmät.

Mikään yksittäinen paikannusteknologia ei sovellu kaikkiin ympäristöihin ja sovelluksiin. Parasta tarkkuutta vaativiin sovelluksiin tarvitaan eri teknologia kuin sovelluksiin, joissa paikannuspalvelun tulee toimia laajalla alueella. Monet nykyisistä sisätilapaikannusjärjestelmistä perustuvat langattoman lähiverkon (wireless local area

network, WLAN) käyttöön. Tässä tutkimuksessa korrelaatio- eli sormenjälkipaikannukseen (location fingerprinting) perustuva paikannus todettiin sisätiloihin sopivimmaksi

WLAN-pohjaiseksi paikannusmenetelmäksi. Uusimmista teknologioista ultralaajakaistaisen (ultra-wideband, UWB) signaalin kulkuajan mittaamiseen perustuva paikannusteknologia todettiin lupaavimmaksi. UWB-signaalin lyhyt pulssi mahdollistaa tarkan saapumisajan mittauksen ja hyvän suorituskyvyn olosuhteissa, joissa esiintyy monitieheijastuksia. Toisaalta rajatuille alueille tarkoitettuihin sovelluksiin voivat sopia myös muut menetelmät, kuten sijainnin haku videokuvasta tai vektoripaikannus (dead-

reckoning).

Tämän tutkimuksen perusteella on nähtävissä, että satelliittipaikannuksen ja paikallisten navigointiverkkojen yhteiskäyttö tulee olemaan eräs keskeisimmistä tutkimuksen aiheista. GPS:n ja esimerkiksi UWB-paikannuksen yhdistäminen yhdeksi paikannuspalveluksi avaisi merkittäviä uusia markkinoita.

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Preface

First, I would like to express my appreciation to the supervisor of this research,

Professor Markku Kivikoski of Tampere University of Technology. His guidance and the excellent lectures given in Seinäjoki have made this research possible. I am also thankful to the second supervisor of this thesis, Professor Jarmo Takala of Tampere

University of Technology.

Also my employer, Seinäjoki University of Applied Sciences, has supported this research. I am especially grateful to the directors of the School of Engineering, Dr.

Tech. Jukka Aarnio and Lic. Tech. Jorma Nevaranta for enabling to write this thesis.

Finally, I would like to thank my wife M.S.S., M.A. Maria Mäkelä for helping me to improve the language of this text, and my children Anna, Ossi and Elina for their understanding and patience.

Seinäjoki, May 2008

Petteri Mäkelä

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ix

Table of Contents

ABSTRACT .................................................................................................................. III

TIIVISTELMÄ ............................................................................................................... V

PREFACE .................................................................................................................... VII

LIST OF FIGURES ................................................................................................... XIV

LIST OF TABLES ..................................................................................................... XVI

LIST OF ABBREVIATIONS ............................................................................... XVIII

LIST OF SYMBOLS ............................................................................................... XXII

1 INTRODUCTION ................................................................................................... 1

1.1

B

ACKGROUND

.................................................................................................... 2

1.2

R

ESEARCH

O

BJECTIVES AND

C

ONTRIBUTION

..................................................... 3

1.3

S

COPE OF THE

R

ESEARCH

................................................................................... 3

1.4

M

AIN

R

ESULTS

.................................................................................................. 4

1.5

O

UTLINE OF THE

T

HESIS

..................................................................................... 5

2 PRINCIPLES OF LOCAL POSITIONING SYSTEMS ..................................... 7

2.1

I

NTRODUCTION

................................................................................................... 7

2.2

D

EFINITION OF

L

OCAL

P

OSITIONING

S

YSTEM

.................................................... 7

2.3

N

EED FOR THE

L

OCAL

P

OSITIONING

S

YSTEMS

................................................... 8

2.4

T

AXONOMY OF

P

OSITIONING

S

YSTEMS

.............................................................. 9

2.4.1

Classification Based on Type of the Location Information ......................... 10

2.4.2

Classification Based on where Position Estimation Takes Place ............... 11

2.4.3

Classification Based on Scale of the Positioning System ........................... 12

2.4.4

Classification Based on Signal Technologies ............................................. 12

2.4.5

Classification Based on Observables .......................................................... 13

2.4.6

Classification Based on Location Estimation Techniques .......................... 13

2.5

A

CCURACY

M

ETRICS FOR

P

OSITIONING

S

YSTEMS

........................................... 14

2.5.1

Root of Mean Square Error ........................................................................ 15

2.5.2

Distance Root Mean Square Error ............................................................. 15

2.5.3

Circular Error Probability .......................................................................... 16

2.5.4

The Effect of Geometry and Range Error Factors ...................................... 16

2.6

O

THER

P

ERFORMANCE

M

ETRICS

...................................................................... 18

2.7

P

RIVACY OF

L

OCATION

I

NFORMATION

............................................................. 19

x

3 POSITIONING SENSORS AND SIGNALS ...................................................... 23

3.1

R

ADIO

F

REQUENCY

.......................................................................................... 23

3.1.1

Modeling Radio Signal Propagation .......................................................... 24

3.1.2

Multipath ..................................................................................................... 24

3.2

I

NFRARED

-

BASED

S

YSTEMS

............................................................................. 25

3.3

U

LTRASOUND

................................................................................................... 26

3.4

O

PTICAL

........................................................................................................... 27

3.5

I

NERTIAL

N

AVIGATION

S

YSTEMS

..................................................................... 28

3.6

DC E

LECTROMAGNETIC

................................................................................... 29

4 OBSERVABLES ................................................................................................... 31

4.1

P

ROXIMITY

....................................................................................................... 31

4.2

R

ECEIVED

S

IGNAL

S

TRENGTH

.......................................................................... 32

4.3

A

NGLE OF

A

RRIVAL

......................................................................................... 34

4.3.1

Signal Strength Direction Finding .............................................................. 35

4.3.2

Phase Difference Direction Finding ........................................................... 36

4.3.3

Doppler Direction Finding ......................................................................... 36

4.4

T

IME OF

A

RRIVAL

............................................................................................ 37

4.4.1

Measuring Time of Arrival at Multipath Conditions .................................. 37

4.4.2

TOA Measurement Methods........................................................................ 42

4.5

TDOA M

EASUREMENT

M

ETHODS

................................................................... 48

5 LOCATION ESTIMATION ALGORITHMS ................................................... 51

5.1

T

IME OF

A

RRIVAL

T

ECHNIQUES

....................................................................... 51

5.2

T

IME

D

IFFERENCE OF

A

RRIVAL

P

OSITIONING

T

ECHNIQUES

............................. 56

5.2.1

Taylor Series Method for Hyperbolic Equations ........................................ 57

5.2.2

Comparing Taylor Series Method to Other Methods.................................. 59

5.3

A

NGLE OF

A

RRIVAL

T

ECHNIQUES

.................................................................... 61

5.4

L

OCATION

F

INGERPRINTING

............................................................................ 63

5.4.1

Offline Phase ............................................................................................... 65

5.4.2

Online Phase ............................................................................................... 66

5.4.3

Improvements to the NNSS Method ............................................................ 67

5.4.4

Effect of the Environment and Infrastructure on Performance .................. 67

5.4.5

Radio Propagation Model ........................................................................... 70

5.4.6

Aliasing ....................................................................................................... 70

6 INDOOR GPS ....................................................................................................... 73

6.1

I

NTRODUCTION

................................................................................................ 73

6.2

A

SSISTED

GPS

AND

H

IGH

S

ENSITIVITY

GPS ................................................... 74

xi

6.2.1

GPS Signal Acquisition ............................................................................... 74

6.2.2

Assisted GPS ............................................................................................... 76

6.2.3

Weak GPS Signal Processing ..................................................................... 76

6.3

P

SEUDOLITES

................................................................................................... 77

6.4

GPS R

EPEATER

................................................................................................ 78

7 LOCAL POSITIONING SYSTEMS ................................................................... 79

7.1

U

LTRASOUND

-B

ASED

P

OSITIONING

S

YSTEMS

.................................................. 79

7.1.1

MIT Cricket System ..................................................................................... 80

7.1.2

Active Bat System ........................................................................................ 81

7.2

WLAN-B

ASED

P

OSITIONING

S

YSTEMS

............................................................ 81

7.2.1

Aeroscout Positioning System ..................................................................... 82

7.2.2

Ekahau System ............................................................................................ 84

7.3

UWB-B

ASED

P

OSITIONING

S

YSTEMS

.............................................................. 86

7.3.1

Advantages of UWB .................................................................................... 87

7.3.2

Applications ................................................................................................ 87

7.3.3

UWB in Positioning .................................................................................... 88

7.3.4

Standards and Regulations ......................................................................... 89

7.4

F

USING

P

OSITIONING

T

ECHNOLOGIES

.............................................................. 94

8 CONCLUSIONS AND DISCUSSION ................................................................ 97

8.1

C

OST AND

M

ARKET

I

SSUES

.............................................................................. 97

8.2

A

CCURACY AND

A

VAILABILITY

R

EQUIREMENTS OF

L

OCATION

-B

ASED

S

ERVICES

......................................................................................................... 98

8.3

C

OMPARISON OF THE

S

IGNAL

T

ECHNOLOGIES

............................................... 100

8.4

C

ONCLUSIONS

................................................................................................ 101

8.5

F

UTURE

R

ESEARCH

........................................................................................ 104

9 SUMMARY ......................................................................................................... 107

REFERENCES ............................................................................................................ 109

xii

xiii

xiv

List of Figures

Figure 1.

Relation between received signal strength and distance. ............................ 32

Figure 2.

Intersection of three circles ......................................................................... 33

Figure 3.

Location solution derived from angle of arrival measurements ................. 34

Figure 4.

Correlation of DSSS signal ......................................................................... 39

Figure 5.

Approximate range resolution versus signal bandwidth ............................. 40

Figure 6.

Estimated TOA of the DLOS path and normalized time domain responses obtained using three different techniques. ................................ 41

Figure 7.

Signal propagation delay estimation in one-way ranging ........................... 43

Figure 8.

Two-way ranging ....................................................................................... 45

Figure 9.

Symmetric double sided two-way ranging ................................................ 48

Figure 10.

Intersection of two hyperbolas ................................................................... 49

Figure 11.

Cross correlation method for TDOA estimation ....................................... 49

Figure 12.

Trilateration by using three measurements ................................................ 51

Figure 13.

Hyperbolic positioning .............................................................................. 57

Figure 14.

AOA Positioning technique ....................................................................... 61

Figure 15.

The geometry of AOA location method .................................................... 62

Figure 16.

Example of location fingerprints estimated ............................................... 65

Figure 17.

Two-dimensional acquisition search space ............................................... 75

Figure 18.

Aeroscout system components .................................................................. 83

Figure 19.

Ekahau system components ....................................................................... 85

Figure 20.

FCC definition of UWB ............................................................................. 86

Figure 21.

Multipath effect of narrowband signal and ultra-wideband signal ............ 88

Figure 22.

FCC Emission masks for indoor and handheld devices ............................ 90

Figure 23.

ECC draft spectrum mask .......................................................................... 91

Figure 24.

Illustration of the IEEE802.15.4a ranging protocols ................................. 94

Figure 25.

Comparison of positioning technologies ................................................. 100

xv

xvi

List of Tables

Table 1.

Relationship between RMSE, 2 drms and CERP ....................................... 16

Table 2.

Summary of positioning technologies. ...................................................... 101

Table 3.

Merits and drawbacks of the positioning technologies ............................. 102

xvii

xviii

List of Abbreviations

AGPS

AOA

API

BER

BS

CDMA

CEPT

C/A

DAA

DC

DDP

DoC

DoD

DOP

DR

CERP

COO

CRLB

CSMA-CA

CSS

DRMS

DSP

DSSS

EC

Assisted Global Positioning System

Angle of Arrival

Application Programming Interface

Bit Error Rate

Base Station

Code Division Multiple Access

European Conference of Postal and Telecommunications

Administrations

Circular Error Probability

Cell of Origin

Cramer-Rao Lower Bound

Carrier Sense Multiple Access with Collision Avoidance

Chirp Spread Spectrum

Coarse/Acquisition

Detect and Avoid

Direct Current

Dominant Direct Path

Department of Commerce

Department of Defense

Dilution of Precision

Dead Reckoning

Distance Root Mean Square

Digital Signal Processor

Direct Sequence Spread Spectrum

European Commission

HW

IEEE

IMU

INS

ISM

LAN

LBS

LDC

LPS

LOS

LR-WPAN

LS

MEMS

FCC

FHSS

GDOP

GNSS

GPS

HDOP

HSGPS

HTTP

ECEF

EGNOS

EIRP

ETSI

EU

E112

E911

E-OTD

Earth Centered Earth Fixed

European Geostationary Navigation Overlay System

Equivalent Isotropically Radiated Power

European Telecommunications Standards Institute

European Union

Enhanced 112 emergency call requirements

Enhanced 911 emergency call requirements

Enhanced Observed Time Difference

Federal Communications Commission

Frequency Hopping Spread Spectrum

Geometric Dilution of Precision

Global Navigation Satellite System

Global Positioning System

Horizontal Dilution of Precision

High Sensitivity GPS

Hypertext Transfer Protocol

Hardware

The Institute of Electrical and Electronics Engineers

Inertial Measurement Unit

Inertial Navigation System

Industrial, Scientific and Medical radio frequency bands

Local Area Network

Location-Based Services

Low Duty Cycle

Local Positioning System

Line of Sight

Low Rate Wireless Personal Area Network

Least Squares

Micro Electro Mechanical System xix

RAKE

RDF

RF

RFID

RMSE

RSS

RSSI

RTK

PAN

PDA

PDOP

PPS ppm

PRN

P(Y)

RAIM

RTLS

RTT

RX

SDK

SNR xx

MIT

MS

MUSIC

NAVSTAR

NDDP

NLOS

NNSS

OTD

Massachusetts Institute of Technology

Mobile Station

Multiple Signal Classification

Navigation System by Timing and Ranging

Non-Dominant Direct Path

Non Line Of Sight

Nearest Neighbor in Signal Space

Observed Time Difference

Personal Area Network

Personal Digital Assistant

Position Dilution of Precision

Precise Positioning Service parts per million

Pseudorandom Noise

Precision(Encrypted)

Receiver Autonomous Integrity Monitoring

Radio receiver designed to counter the effects of multipath fading

Radio Direction Finding

Radio Frequency

Radio Frequency Identification

Root Mean Square of Errors

Received Signal Strength

Received Signal Strength Indicator

Real Time Kinematics

Real-Time Location System

Round Trip Time

Radio Receiver

Software Development Toolkit

Signal-to-Noise Ratio

WGS

WLAN

WPAN

XML

2D

3D

UDP

UERE

US

UWB

VDOP

VHF

VOR

WAAS

SPS

SW

TCP/IP

TDOA

TDOP

TG

TOA

TW-TOA

Standard Positioning Service

Software

Transmission Control Protocol/Internet Protocol

Time Difference of Arrival

Time Dilution of Precision

Task Group

Time of Arrival

Two-way TOA

Undetected Direct Path

User Equivalent Range Error

United States

Ultra-wideband

Vertical Dilution of Precision

Very High Frequency

VHF Omnidirectional Range

Wide Area Augmentation System

World Geodetic System

Wireless Local Area Network

Wireless Personal Area Network

Extensible Markup Language

Two Dimensional

Three Dimensional xxi

xxii

ε

meas

c

d

Δρ

r

t

Δx

T



θ

ρ

ˆ

σ

a

r

List of Symbols

Vector of pseudorange measurement errors

Vector of position error components

Oscillator time offset

Phase of the signal

Wavelength

Angle of arrival

Pseudorange

Approximate (predicted) pseudorange

Standard deviation

Standard deviation of the position

Standard deviation of the range

Time of arrival

Phase

Phase difference

Difference between the approximate (predicted) pseudorange and the true pseudorange

Vector of

i

’s

Vector of differences between the measured TDOA and predicted

TDOA

Time difference

Vector offset of the user’s true position and time bias from the values at the linearization point

Speed of light

Distance

F

G

H

n

P

0

T reply

T round

T t v us

R

SE t t u

T

r

R r

rˆ

~

R x measured x true x i

,

y i

,

z i x u

,

y u

,

z u

ˆ

u

,

u

,

z

ˆ

u

Carrier frequency

Design matrix for AOA method

Design matrix for TDOA method

Design matrix for TOA method measurement error of angle of arrival

Empirical coefficient

Received signal strength

Range

Approximate (predicted) range

Residual vector

Vector of RSS samples measured at online phase in LF method xxiii

Vector of RSS samples measured at offline phase in LF method

Square of residual vector length

Time

Time offset between the mobile terminal clock and the system time

Temperature

Reply time of the signal

Round trip time of the signal

Time of flight of the signal

Speed of sound

Measured location

True location

Location of the ith base station

Location of the user or mobile station

Approximate (predicted) location of the user

1

1 Introduction

Due to the increasing number of mobile computing devices and wireless local-area networks, a growing interest in location-aware systems and services has emerged.

Commercial location-based services, such as map-based guidance and navigation, intelligent transport services and tracking of people and valuable assets, have a huge market potential. The recent company acquisitions in the telecommunications and navigation industry especially emphasize this market potential (Nokia, 2007).

In order to serve their users well, emerging mobile computing applications will need to know the physical location of people and assets. Today the most common technology to provide the physical location information is Global Positioning System (GPS). In the future there may also be other satellite navigation systems, for example European

Galileo. However, there is also a growing interest in positioning applications which are independent from global satellite navigation systems. These positioning systems, which are usually designed to operate inside a limited geographical area, are called

local positioning systems.

The most important reason to use local positioning systems is the relatively poor performance of GPS inside the buildings and urban canyons. Despite the development of high-sensitivity GPS receivers and assisted GPS (AGPS) technologies, the performance of GPS is not sufficient for many indoor applications. To obtain location information also inside buildings, several indoor positioning systems have been developed.

The need for the local positioning systems is not only restricted to the indoor environment. Local positioning systems are used in applications where people or objects are tracked both indoor and outdoor environments. For example, in Legoland

Billund the parents can track the movements of their children in the amusement park area by using the local positioning system based on wireless local area network

(WLAN) infrastructure (InformationWeek, 2004).

The cost of the positioning technology is also an important factor. Sometimes local positioning systems are used because the sensors to be tracked in the local positioning system are less expensive than the GPS receivers. For some organizations,

2 independence from global satellite navigation systems may be a good reason to use local positioning systems.

In addition to the global satellite navigation systems, there are many different local positioning and indoor navigation technologies. None of these systems has optimal performance in all circumstances where location-aware services are needed. Different positioning technologies have to be used with different applications. However, there is a need for a location-based service, which would provide accurate position data both indoors and outdoors. Which local positioning technologies will be integrated with global positioning technologies, remains to be seen in the future.

1.1 Background

The first indoor location systems were developed some 20 years ago. The first systems were intended for research purposes in laboratories and they used pattern matching, ultrasound propagation or magnetic field as a navigation observable. Early indoor location systems were either too expensive or too sensitive to electromagnetic interferences, and thus not suitable for industrial or consumer markets.

Since the late 1990’s less expensive and more robust indoor navigation systems have appeared in the market. Most of these systems utilize wireless LAN or other local radio network. In WLAN-based systems, the position of a mobile terminal is most often calculated from the received signal strength (Ekahau, [Cited January 24, 2008]).

However, received signal strength (RSS) indicator does not provide a good estimate for the distance, but the RSS may give a reasonable accurate position fix if it is correlated to a map of previously measured signal strengths. This type of positioning method is called location fingerprinting. The problem of the location fingerprinting technique is that it requires extensive calibration work before the location service can be used.

The other type of WLAN-based system measures the time difference of arrival

(TDOA) of a mobile WLAN transmitter (Aeroscout, [Cited January 24, 2008]). In these systems additional hardware and signal processing is needed, since the standard

WLAN access points are not able to perform the TDOA measurements.

The two major error sources in TDOA estimation at the indoor environment are multipath fading and no line of sight (NLOS) condition due to shadow fading

(Pahlavan 2002). Because of the multipath problem present in indoor environment, traditional cross correlation techniques of wide-band direct sequence spread spectrum

3

(DSSS) signal do not perform well (Pahlavan & al., 2002). Performance of TOA estimation of wide-band signal can be improved by employing super-resolution TOA estimation techniques or increasing the signal bandwidth.

In ultra-wideband (UWB) technology extreme short duration pulses (sub-nanosecond) are used instead of continuous waves to transmit information. The short pulse generates directly a very wide bandwidth signal. One important advantage in indoor navigation and communication is that UWB is relative immune to multipath fading

(Sahinoglu & Gezisi, 2006). The problem in the ultra-wideband technology is the limited range of the signal.

1.2 Research Objectives and Contribution

The objective of this research is to identify the most promising current and emerging local positioning techniques. The research also aims to match the characteristics of the identified local positioning techniques to the needs of various location-based applications. These needs depend considerably on the physical environment where the local positioning systems are used.

This licentiate thesis is a literature research. The aim of the research is to make a comprehensive survey on the field of local positioning systems. A classification of the local positioning systems is presented to find a match between the application needs and properties of the local positioning systems. The principles of the localization are discussed and various positioning technologies are presented. Also the most common location estimation algorithms are presented in such level of details that the algorithms can be easily converted to a computer program.

1.3 Scope of the Research

The overall purpose of the research is outlined in its title, but a closer look into the scope is discussed in this section. This research discusses local positioning systems and

indoor navigation.

In this thesis the local positioning system refers to the positioning services, which are available only within a limited geographical area. Global positioning systems and services, such as NAVSTAR GPS, are out of scope in this research, with the exception of the indoor use of GPS signals. Also the positioning services based on the cellular network signals are considered as ―global‖ positioning services, and thus they are out of the scope in this research.

4

The other part of the title is indoor navigation. In this thesis, indoor navigation is considered as a special case of the local positioning. Several positioning techniques developed for the indoor applications are discussed. In spite of that the NAVSTAR

GPS is not a local positioning system, the assisted GPS technology and highsensitivity GPS (HSGPS) are explained in this thesis. The indoor use of the

NAVSTAR GPS can be considered as a competing technology to the local positioning systems intended for indoor environment.

A wide range of positioning methods such as dead-reckoning, ultrasound-based systems and use of optical sensors, are discussed. However, the most commonly used local position services are based on the use of radio frequency signals. The other sensors will provide only supplementary information for the positioning service. Thus, the main emphasis of this research is in the positioning observables and algorithms of the radio frequency devices.

Only the most commonly used positioning algorithms are explained in detail in this thesis. In the systems based on trilateration, the position estimation algorithm based on

Taylor series expansion is most widely used in real applications. In the literature, there are also many other algorithms presented, whose performance over the Taylor series method is often questionable or whose implementation is relatively complex. These algorithms are discussed only shortly, and references to them are given to the reader.

1.4 Main Results

No single technology was found which would have provided optimal performance for all possible applications and environments. The technology based on location fingerprinting method was found the most promising technology in heavily multipath environment, such as office buildings. On the other hand, the technology based on measuring the WLAN signal propagation time performs better in large open areas, such as sport halls. Neither of these WLAN-based technologies is feasible, if better than 1–2 meters accuracy is required. When accuracy is important, utilizing UWB signals for positioning will be an interesting alternative. However, opposite to WLAN,

UWB is not a mature technology yet and in Europe the legislation processes of UWB signals are still going on. When only a building level accuracy is sufficient indoors, use of the assisted GPS can be recommended.

Several positioning technologies based on other positioning signals than radio frequency were also identified. For example, the location of an object can be determined with few centimeters accuracy by using computer vision system and video

5 image analysis. Also dead-reckoning method can be considered in applications where the calibration to the local frame of reference is not a problem. However, the RF-based local positioning systems are more popular because of the excellent coverage of the radio signal.

Fusing satellite-based navigation system with local positioning systems is suggested as a future research topic. Another interesting future research topic is better utilization of redundant measurements in location fingerprinting systems.

1.5 Outline of the Thesis

Chapter 1 reviews the background and purpose of this work. The main objectives and research questions are formulated. Finally, the scope and limitations of the work are defined.

Chapter 2 defines the concept of the local positioning system. It also defines the taxonomy of the local positioning systems and shows examples of applications utilizing the local positioning technologies. Finally, the performance criteria for evaluating the performance of the position systems are introduced.

Chapter 3 presents the positioning sensors and metrics used in the positioning.

Positioning sensors and systems based on infrared, ultrasound, DC electromagnetic, optical, gyroscope and radio frequency signals are explained.

Chapter 4 presents the observables used in positioning systems based on the use of the radio frequency signals. The following observables are explained: received signal strength (RSS), angle of arrival (AOA), time of arrival (TOA), and time difference of arrival (TDOA). Also proximity-based methods and location fingerprinting methods are explained.

Chapter 5 reviews the position estimation algorithms, which compute the location of a mobile terminal from the observables presented in chapter 4.

Chapter 6 illustrates the use of the indoor GPS as an alternative to the local positioning systems.

Chapter 7 presents the most promising current and emerging local positioning systems.

Examples of local positioning systems based on ultrasound, WLAN and ultrawideband signals are given.

6

Chapter 8 contains the discussion and conclusions.

Chapter 9 summarizes the results.

7

2 Principles of Local Positioning Systems

2.1 Introduction

This chapter provides an overview of the applications which utilize the local position systems. A user’s location will become common information in the near future. GPS chips and other navigation devices have become smaller and less expensive. They also consume substantially less energy than 10 years ago. Furthermore, the WLAN-based positioning systems requiring no extra components at the mobile device have been developed during the recent years. This development in the navigation systems makes it possible to equip even smaller and less expensive devices with the knowledge of their location. This opens completely new application areas for the location-based systems.

Traditionally, positioning systems have been used in sea and air navigation and surveying. Consumer level navigation devices became common after the mid-1990’s, when GPS became operational. Markets of the handheld and car installed GPS devices have been growing since that. However, in many applications the positioning information is not interesting if this information cannot be communicated to other users or devices. Until the early 2000’s the GPS receivers have been too expensive and too power consuming to be integrated in to a mobile phone. The first high volume mobile phones containing an integrated GPS receiver came to European markets only in 2007. Integrating positioning technology with the communications technology provides many new applications for the consumer and industrial markets.

Even though modern GPS receivers are much more sensitive than GPS receivers in the

1990’s and even though they are able to locate indoors, the accuracy and reliability of

GPS is not sufficient for many indoor applications. Local positioning systems are needed for applications that are used in environment where the GPS signal is weak.

2.2 Definition of Local Positioning System

There is no commonly accepted definition for the local positioning systems. In this thesis the local positioning system refers to the localization services, which are available only within a limited geographical area. In the literature, the term local

8 positioning system usually refers only to indoor navigation. However, there is a need to deploy proprietary positioning services for small geographical areas, which may be located either indoors or outdoors – or both.

The local positioning systems have the following properties:

Short-range signals are used and thus the positioning service is available only within a relatively small geographical area

The positioning infrastructure is often maintained locally by an organization that operates at the same area where the local positioning service is available.

The positioning service is often independent on the global positioning services or positioning services provided by telecommunications operators

Locally defined coordinate frame of reference is often used

A local positioning system can be considered as contrary to a global positioning system. Here, a global positioning system is considered to be a globally available service, which is operated by governments or by large organizations such as telecommunications operators. NAVSTAR Global Positioning System, which is operated by the Department of Defense (DoD) of the United States, is the most well known example of global positioning systems. Other examples of global or ―semiglobal‖ navigation systems are LORAN-C and VHF Omni-directional Radio Range system (VOR). The positioning services dependent on the cellular network signals may be considered semi-global positioning services as well.

2.3 Need for the Local Positioning Systems

Today, the most common reason to deploy a local positioning service is the poor performance of NAVSTAR GPS at inside buildings and urban canyons. Most of the local positioning systems used are either indoor navigation systems or systems based on dead-reckoning. However, the need for the local positioning systems is not restricted to the indoor environment alone. There is a need for positioning services, which are able to provide accurate positioning information both indoor and outdoor areas. The high-sensitive GPS receivers provide high accuracy position data outdoors, but the positioning accuracy for certain indoor location-aware applications is relative poor. Local positioning system based on WLAN signals may provide a better performance in the environment that consists of indoor and outdoor areas.

In wireless sensor network applications the mobile nodes must be extremely low-cost and energy-efficient. Today, the prices of GPS modules intended for mobile devices

9 are relative high, varying from 5 € to 30 €, depending on the volumes purchased. The

GPS receivers are also too power-hungry for the nodes of sensor network. The local positioning techniques developed can provide a cost-efficient solution for indoor and outdoor wireless sensor network applications. For some organizations, independence from global satellite navigation systems may be a good reason to use local positioning systems.

2.4 Taxonomy of Positioning Systems

There are many different positioning technologies developed to locate people and valuable assets. Each positioning technology solves a slightly different problem or supports a different kind of location-aware application. These positioning technologies vary in many parameters, such as the physical phenomena used for location determination, the size of the sensing device, energy consumption, and resolution in time and space.

Different kinds of taxonomies have been developed for positioning systems

(Hightower & Borriello, 2001; Zeimpekis & al., 2003; Muthukrishnan & al., 2005;

Kolodziej & Hjelm, 2006). According to Hightower & Borriello (2001), the purpose of these taxonomies is to help developers of location-aware applications evaluate their options better when choosing a positioning system. The taxonomy may also help researchers in identifying opportunities for new positioning techniques.

In this research, the following taxonomy is presented:

Classification based on type of the location information. A positioning system may provide physical or symbolic location information. On the other hand, the physical coordinates may be given in absolute (global) or relative frame of reference.

Classification based on where location estimation takes place. The location of the mobile terminal may be estimated either in the mobile terminal itself or in the network server associated with positioning system infrastructure.

Classification based on scale of the positioning system. The coverage of a positioning system may be worldwide or it may be able to locate objects within a limited geographical area only.

Classification based on signal technologies. Most of the positioning systems are based on radio signal. However, also magnetic compass, gyroscope, video camera picture or ultrasound pulses can be employed.

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Classification based on observables. The position of the object can be derived from time of arrival (TOA), time difference of arrival (TDOA), or angle of arrival (AOA) observables.

Classification based on location estimation techniques. Triangulation and trilateration methods compute the position of the target object by using ranging measurements. There are also range free methods, such as proximity-based methods and location fingerprinting.

2.4.1 Classification Based on Type of the Location Information

Hightower & Borriello (2001) classify the positioning systems in two categories according to the type of the location information. The positioning system may provide either physical coordinates or symbolic location of the target device. The location information in physical coordinates can be classified further in two categories: absolute and relative.

The physical coordinates, having numerical values, may be given in absolute or relative frame of reference. In location-aware applications Cartesian or polar coordinate system is usually used. In contrast to the physical coordinates, the symbolic location tells to the user, in which place the target object is. The symbolic location may refer to a particular room or building or it may tell that the target object is approaching to the point of interest. The physical location information can be usually transformed to symbolic location information by using a database containing interesting places and their physical coordinates (Hightower & Borriello, 2001).

The most well known positioning systems, like GPS and LORAN-C, are clearly physical-coordinate positioning technologies. On the other hand, many technologies provide inherently symbolic location information. Bar code scanners and access control terminals provide symbolic location information based on proximity to known objects (Hightower & Borriello, 2001).

The physical-positioning system must be accurate enough, so that it can place the target object to the correct symbolic location. For example, positioning system having

10 meter accuracy is not very useful, if the symbolic locations are office rooms having size of 3 x 3 meters. On the other hand, purely symbolic location systems typically provide only very coarse-grained physical positions (Hightower & Borriello, 2001).

They may tell that the target object has passed a certain access control terminal, and thus it must be in a certain room in the building. However, it is impossible to tell in which part of the room exactly the target object is.

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The physical-positioning systems can be further classified in two categories: the systems providing absolute and relative coordinates (Muthukrihnan & al., 2005,

Hightower & Borriello, 2001). An absolute positioning system uses a shared frame of reference for all located objects, while relative positioning system has its own frame of reference. GPS and LORAN-C are examples of absolute positioning systems. GPS provides location data in WGS-84 frame of reference. A relative location can be converted into an absolute position if the translation, rotation and scaling matrices between the reference frames are known.

2.4.2 Classification Based on where Position Estimation Takes Place

The position of the user may be calculated either at the user’s terminal or at a remote computer. Systems where the position calculation of the device takes place in the device itself are called self positioning systems, while systems where the position of the object is calculated at a remote site are called remote positioning systems

(Zeimpekis & al., 2003).

Often the remote positioning systems are referred as network-

centric or network-based systems.

In the self positioning systems, the mobile device being located calculates its own position. The base stations can be either terrestrial or satellites, and the locations of the base stations are known. The self positioning model ensures privacy by mandating that no one else may know where the mobile device is, unless the object specifically takes action to publish its location (Hightower & Borriello, 2001). Also the raw measurements, from which the location is computed, are not known by other entities in the system. The mobile terminal receives the signals transmitted from the base stations and calculates its own position by using this data. GPS is an example of the self positioning systems.

In the network-based system, the mobile terminal can be located by measuring signals traveling to and from a set of base stations. The position estimation takes place at a remote site, which is often a network server. If the mobile terminal transmits the navigation signal and the signal is received at the base station, the system is inherently network centric. Also the pure symbolic positioning systems, which employ devices such as access control terminals or radio frequency identification tags, are obviously network centric. Indoor positioning systems utilizing the WLAN signal are typically network-based.

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2.4.3 Classification Based on Scale of the Positioning System

One of the classification argument presented by Hightower & Borriello (2001) is the scale of the location system. The scale of the positioning system can be associated to the size of the geographical area the system serves. Moreover, the scale of the positioning system is associated to the number of the objects the system is able to locate with a certain amount of infrastructure or over a given time.

A global system like GPS is able to locate objects worldwide, while the service of a local positioning system is limited within a certain area only. Local positioning systems may be able to locate objects within a certain part of a city, in a particular building, or within a single room. Usually the number of objects located is not limited in the self positioning systems, like GPS and Cricket indoor positioning system

(Priyantha, 2005). On the other hand, in network centric systems, the number of mobile terminals is limited due to the restricted server capacity and bandwidth available in communication channel. Because of these limitations, the base station can receive only a certain amount of positioning signals per time interval.

2.4.4 Classification Based on Signal Technologies

Muthukrishnan & al. (2005) identifies the following signal technologies used in positioning systems:

Radio frequency. Radio navigation uses radio waves to determine the user’s position. Using radio signals has been popular in navigation, because of radio wave’s ability to travel long distances. Most of the indoor positioning systems are based on radio signals as well.

Infrared. Infrared positioning systems are based on proximity, not ranging. The location of the mobile device is assumed to be the same as the location of the base station emitting the infrared signal.

Ultrasound. Ultrasound technology provides a great ranging accuracy with low cost electronics. High probability of interference from other ultrasound sources reduces the reliability of the ultrasound-based systems.

Optical. The location information can be derived from analysis of video images.

Inertial. Inertial navigation systems collect information from gyroscopes and accelerometers to determine the position and orientation of the device.

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DC Electromagnetic. The direct current (DC) electromagnetic systems generate axial DC magnetic-field pulses from a transmitting antenna in a fixed location.

The system measures the position and orientation of one or more receiving antenna sensors with respect to transmitting antenna.

These signal technologies are explained in more detail in Chapter 3.

2.4.5 Classification Based on Observables

The positioning systems can be classified according to observables used in position determination. The most common observables are:

Time of arrival (TOA). A range between the transmitter and receiver is estimated by measuring the signal propagation delay between these two devices.

Time difference of arrival (TDOA). Difference of arrival times of two signals is measured. The TDOA measurement and the known coordinates of the two base stations define a hyperbola. The position of the object is obtained from the intersection of multiple hyperbolas.

Angle of arrival (AOA). Direction angle of the signal is measured. Each estimated angle defines a line between the base station and a mobile device.

The location of the object is obtained from the intersection of these lines.

Received signal strength indicator (RSSI). The intensity of a transmitted signal decreases as the distance from the transmitter increases. The range between the transmitter and receiver can be estimated from the received signal strength.

Muthukrishnan & al. (2005) use the term ranging technology in context of the observables mentioned above. However, the term ranging technology is somewhat misleading, since the angle of arrival method provides an angle measurement instead of a range measurement. On the other hand, positioning systems based on RSSI do not usually perform geometric ranging. Instead, the position estimate is obtained by location fingerprinting algorithm, which is explained in the next section.

2.4.6 Classification Based on Location Estimation Techniques

Hightower & Borriello (2001) divide the positioning systems into three main categories: triangulation-based systems, proximity-based systems, and scene analysis systems. In addition to these techniques, location fingerprinting and dead-reckoning can be considered as examples of location estimation techniques too.

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Muthukrishnan & al. (2005) subcategorize the triangulation into lateration and

angulation. Trilateration method computes the position of an object by measuring its distance from multiple reference positions. In three dimensional positioning, three distance measurements are needed to solve the position of the object. The position of the object is obtained from the intersection of spheres whose centers are at the reference positions and whose radius is the measured distance.

In triangulation method, angles of arrived signals are used for determining the position of an object. Each measured angle defines a line between the base station and the target object. The location of the object is obtained from the intersection of these lines.

Proximity measures the nearness to a known point or set of points. Knowing only whether or not two devices are in communication range is enough to give a position estimate. In this method the location of the target object is considered to be the location of the base station. If a signal is received at several known locations, it is possible to intersect the coverage areas of that signal to determine a ―containing‖ location area (Kolodziej & Hjelm, 2006, p. 145). Another method is to choose the location of the base station which has the strongest signal as the target object location.

Scene analysis examines a view from a particular vantage point to draw conclusions about the observer’s location. Video cameras can be used to describe spatial relationship in scenes using image processing techniques, and thereby determine the position (Hightower & Borriello, 2001).

In location fingerprinting method, the position estimate is obtained by comparing the measured RSS values to the database containing previously measured RSS values at each place in the positioning area. In the dead-reckoning (DR) method, the current position estimate of the target object is based upon a previously determined position estimate. This position estimate is then advanced by using the known speed, elapsed time, and course.

Location Estimation techniques are discussed in more detail in Chapter 5.

2.5 Accuracy Metrics for Positioning Systems

Generally, the most important performance metric for the positioning systems is the accuracy of the estimated position.

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Accuracy is usually reported as the error distance between the estimated location and the actual location, while a location precision is reported in percentages of position information, which is within the distance of accuracy. A self explanatory definition for accuracy is defined in (CDG, 2000), as: ―Accuracy of the geolocation technology is a measure that defines how close the location measurements are to the actual location of the mobile station to be located‖.

2.5.1 Root of Mean Square Error

The root of mean square error (RMSE) is a widely used measure of the difference between the true location and multiple of measured locations. RMSE is calculated from square root of the mean value of the squared error:

RMSE

N

k

1

(

x measured

(

k

)

N

x true

)

2

(2.1) where N is the number of measurements in the set and k is the index of the measurement. RMSE equals the standard deviation of the measurements.

The accuracy metric can be defined for various dimensions. Three-dimensional accuracy is evaluated by taking into account all position dimensions. The result is independent from coordinate system. Two-dimensional accuracy refers to horizontal accuracy. One-dimensional accuracy refers either to vertical accuracy measuring deviations in altitude or to radial accuracy measuring deviations in distance from the true location to the measured location (Syrjärinne, 2001, p. 8).

2.5.2 Distance Root Mean Square Error

The distance root mean square error (drms) in two dimensions is defined by formula

(Kaplan, 1996, p. 280):

drms

2

x

2

y

(2.2) where

x

and

y

are the standard deviation (RMSE) of the error along the x and y axes.

The probability that the computed location is within a circle of radius drms from the true location is about 0.63 if the error distribution in both axis is about the same.

Probability that the error is within a circle of radius two times drms (2drms) varies

16 between 95% and 98% depending on the difference of error distributions on the coordinate axis. The 2drms value is commonly taken as the 95% limit for the magnitude of the horizontal error (Kaplan, 1996, p. 280).

2.5.3 Circular Error Probability

A second accuracy measure in common use is the circular error probable or circular error probability (CERP). The CERP is defined as the radius of a circle that contains at least 50% of the measurements. Thus, probability that the magnitude of the error is less than the CERP is precisely ½. In addition to 50%, CERP is sometimes defined also to other probabilities. Kaplan (1996, p. 281) gives a following approximation for relationship between the CERP and drms:

CERP

50

CERP

80

CERP

95

0

.

75

drms

1 .

28

drms

2 .

0

drms

Table 1 summarizes the relationship between the RMSE, 2 drms and CERP.

Table 1. Relationship between RMSE, 2 drms and CERP

Accuracy Measure

RMSE, drms (root mean square error)

CERP

50

(circular error probability 50%)

CERP

95

(circular error probability 95%)

2 drms

Probability (%)

63 to 68

50

95

95 to 98

2.5.4 The Effect of Geometry and Range Error Factors

The accuracy of the location estimate depends on the accuracy of the individual measurements and the mutual geometry of the mobile object and base stations. If the measurement observable is range, the accuracy of the estimated location depends on the accuracy of the individual range measurements and the mutual geometry of the mobile object and base stations.

In the positioning systems based on range observable the accuracy of the position solution is ultimately expressed as the product of a geometry factor and a range error factor. Error in the position solution can be approximated by the formula (Kaplan,

1996, p. 238):

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(error in position solution) = (geometry factor) * (range error factor)

User Equivalent Range Error

In GPS the effective accuracy of the measured range value is termed the user equivalent range error (UERE) (Kaplan, 1996, p. 238). The UERE for a satellite is considered to be a statistical sum of various error sources associated with the satellite.

Usually the error components of total UERE are considered independent with

Gaussian distribution. UERE is usually assumed to be independent and identically distributed from satellite to satellite. However, it is also possible to model UERE of certain satellites (e.g. EGNOS/WAAS satellites) with a different variance.

In GPS the total range measurement errors consist of the following factors (Kaplan,

1996, p. 239):

delays due to the atmosphere (very small in local positioning systems)

receiver noise and resolution offset

multipath offset

receiver hardware offsets

errors in base station location

errors in system time reference

The error components are root-sum-squared (rss) to form the total system UERE. For a

GPS C/A code the UERE is 8.0 m when Selective Availability is off (1

) (Kaplan,

1996, p. 261). In reality some of the error components, like multipath offset, are not independent nor Gaussian distributed.

In spite of the concept of UERE is used mostly in GPS literature, the UERE concept can be applied to other positioning techniques using the range observable as well.

Dilution of Precision

Dilution of precision is a GPS term which describes the geometric strength of satellite configuration on GPS accuracy. When visible satellites observed from the user position are close together in the sky, the geometry is said to be weak and the DOP value is high. When the satellites are far apart, the geometry is strong and the DOP value is low. The term DOP can also be applied to other positioning systems based on range measurements and to the systems using angle-of-arrival (AOA) measurements

(Dempster, 2006).

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The geometric dilution of position (GDOP) is defined as follows (Kaplan 1996, p.

267):

GDOP

 trace (

H

T

H

)

1

(2.3) where H is the direction cosine matrix containing unit vectors pointing from the linearization point to the location of the ith base station. Direction cosine matrix H is

the same matrix which is used in TOA position calculation explained in Chapter 5.1.

If H is constructed using a local coordinate system (East, North, Up), then similar

DOP factors can be defined for 3D position, horizontal position, vertical position and time. Position dilution of precision (PDOP) is defined by three first diagonal elements, horizontal dilution of precision (HDOP) by the first two, vertical dilution of precision

(VDOP) by the third, and time dilution of precision by the fourth.

The expected value of the positional error can be related to expected range error via

(Dempster, 2006):

a

DOP

r

(2.4) where

r

is the standard deviation of the range and

a

is the standard deviation of the position. This relationship can be expressed also by using terms RMSE and UERE:

RMSE

DOP

UERE

(2.5)

For example, if the expected value of the range error (UERE) is 8 meters and PDOP is

3, the expected position error is 24 meters (drms). Two-sigma value for the position error would be 2 * PDOP * UERE = 48 meters.

2.6 Other Performance Metrics

In addition to the accuracy of the positioning system Syrjärinne (2001) defines four more measurements of performance: reliability, availability, latency and applicability.

In addition to these, Muthukrishnan & al. (2005) have found other performance metrics, such as scalability and privacy. However, the last two metrics are rather properties of positioning systems than performance metrics.

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Reliability is as important performance metric as the accuracy in local positioning systems. Reliability measures the probability of exceptional phenomena, which would totally destroy the location measuring process. Reliability should be automatically taken into account when accuracy of the positioning technology is determined

(Syrjärinne, 2001, p. 10).

Syrjärinne (2001, p. 11) considers availability as a performance metric. Positioning system availability describes whether or how often a system is available for positioning by its intended users. Usually the concept availability also includes the concepts of coverage and capacity (Syrjärinne, 2001, p. 12). Availability measures different aspects of positioning than accuracy and reliability. Syrjärinne gives an example: If a GPS receiver is taken deep underground, the GPS signals cannot be tracked and thus position determination becomes impossible. The fact that signals are blocked does not imply that GPS was not very good in terms of accuracy and reliability. The lack of GPS tracking implies that GPS signals are not available for the user in underground facilities. The concept of performance metric availability relates closely to the taxonomy property scale discussed earlier in this chapter.

Responsiveness (Muthukrishnan & al., 2005) describes how quickly the location system outputs the location information. Syrjärinne (2001) uses term latency for the same purpose.

Applicability measures the physical limitations and requirements associated with the implementation of a certain technology in terms of technical and financial issues

(Syrjärinne, 2001, p. 15). The most important metrics are the cost, power consumption, hardware size and network dependency.

2.7 Privacy of Location Information

Location-based services have a wide field of promising applications. Unfortunately, the emerging location-based services raise a lot of privacy issues due to their ability to collect, store, use, and disclose personal information. These privacy issues represent new challenges to the law makers, location service providers and the users of these systems. To maintain trust and confidence among the different participants, these privacy issues and the associated threats must be addressed (Gadzheva, 2007). If people do not have confidence about their privacy, the widespread deployment of the location-based systems will be hindered.

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Location information of a person is very sensitive and private data. When used together with other information of a person, location information identifies the person and allows his or her whereabouts to be tracked anytime and anywhere. Continuously tracking of a person’s location, storing it centrally and transmitting it to other users potentially compromise an individual’s location privacy (Gadzheva, 2007). To make the users feel confident in using location-based services, they should be informed about the possibilities of the system and be assured that their privacy is well protected.

The user should not only be conscious of the data collection, but also have an opportunity to explicitly give permission to the collection of location data (Baumann & al., 2002).

The distinction between self positioning and network-based positioning is important from the privacy point of view. The self positioning systems, such as GPS, are less risky in terms of privacy, since the measuring of the ranging observables and the position computation are performed by the user’s terminal equipment. This means that the location data are originally in the control of the user and thus not disclosed to location service providers or operators unless the user decides so (Simojoki, 2003).

Most of the local positioning systems are network-based systems. In network-based systems the computation of the location information takes place at a network server maintained by the service provider. For example, the WLAN-based positioning systems on the market are network-based. In addition, many proximity-based systems are also network-based as well as all cellular network-based positioning techniques. In these systems, location data is generated by the service provider and the loss of privacy is higher. Gadzheva (2007) sees the growing use of services based on radio frequency identification (RFID) technology as a potential risk for the location privacy. When combining the RFID techniques with the proximity-based positioning techniques, people can be tracked via their objects or clothing, and a wide array of information can be gathered. Shop owners can create profiles of their customers by tracking their movements in the shops by using RFID.

The network-based approach provides many advantages from the technology point of view. Usually the cost of components and the power consumption are lower, if the mobile device does not contain the hardware needed to detect and measure the ranging signal. Also the position computation requires some processing power and memory, which also raises the cost and adds the power consumption.

Many local positioning systems used today are intended to track things rather than people, and the concerns related to privacy in these systems are naturally obsolete.

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However, when position data of the people is tracked, consent of the user for positioning is always required. The service provider must also be able to guarantee that the data is well protected and not used for any other purposes than the user has accepted. The markets of the indoor and outdoor location-based systems will not develop, if people do not trust the privacy of the location information.

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23

3 Positioning Sensors and Signals

Different types of sensors and signal technologies are used in various positioning systems. In this chapter radio frequency, infrared, ultrasound, optical and inertial signal technologies are presented.

3.1 Radio Frequency

Radio navigation uses radio waves to determine the user’s position. Using the radio signals has been popular in ship and aircraft navigation, because of radio wave’s ability to travel long distances. Most of the indoor positioning systems are based on radio signals as well.

Radio direction finding (RDF) is the oldest method of radio navigation. A radio direction finder works by pointing a directional antenna in various directions and then listening to the direction in which the signal comes through most strongly. Measuring the angle of arrival (AOA) at the base station be can also employed in local positioning systems.

Many radio navigation systems are based on determining the distance between the radio transmitter and the radio receiver. This distance can be derived either from the signal propagation time between the devices, or from the received signal strength

(RSS), which depends on the distance between the transmitter and the receiver.

A GPS receiver measures the time of arrival (TOA) of the radio signal. The signal propagation time between the transmitter and the emitter is obtained by subtracting the known signal transmit time from the measured arrival time. Measuring the signal propagation time is used in some local positioning systems also. Some systems like

Decca, OMEGA and LORAN-C measure the time difference of arrival (TDOA) of two signals instead of the signal propagation time. These systems are called hyperbolic systems, since the coordinates of the two stations transmitting these signals and the measured time difference of arrival defines a hyperbola. The position of the device is obtained from the intersection of two or more hyperbolas.

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Examples of radio navigation systems are

Satellite-based systems: GPS and Galileo

Global terrestrial systems: LORAN-C, VOR

Systems based on cellular phone network signals

Systems based on short range communications signals, such as WLAN, RFID

UWB and Bluetooth

From these technologies WLAN, RFID, UWB and Bluetooth are applicable in local positioning systems. Also the satellite navigation systems like GPS and Galileo can be utilized in indoor navigation. Global terrestrial systems like LORAN-C are not accurate enough for indoor navigation.

3.1.1 Modeling Radio Signal Propagation

Pahlavan & al. (2002) characterize the indoor radio propagation channel as a sitespecific and a severe multipath propagation path between the transmitter and receiver.

Also, inside buildings the availability of a direct line of sight (LOS) signal is relative low. The two major error sources in the indoor environment are multipath fading and no line of sight (NLOS) condition due to shadow fading.

Radio propagation channel models are developed to provide a means to analyze the performance of a wireless receiver. However, the performance criteria for telecommunications and positioning systems are quite different. In telecommunication systems the most important performance criteria is the bit error rate (BER) of the received data stream, while for the positioning systems the performance measure is the accuracy of the estimated position. The accuracy of the estimated position depends on the location metrics used in the positioning technology. The metrics for radio navigation applications are angle of arrival (AOA), received signal strength (RSS) and time of arrival (TOA). According to Pahlavan & al. (2002) there are suitable models, which can be used to analyze the RSS and AOA for indoor positioning systems.

However, the existing wideband indoor channel models designed for the telecommunication applications are not suitable for the analysis of the behavior of

TOA for indoor positioning systems.

3.1.2 Multipath

Multipath is a phenomenon of a radio device receiving the same signal multiple times, slightly offset in time. Multipath phenomenon occurs when a radio signal is received

25 directly from the transmitter, and also reflected off one or more nearby objects, such as walls, roofs or furniture inside a building. Because a reflected signal takes a longer path, it is slightly delayed compared to the direct signal. At the receiver, the multiple copies of the received signal interfere with each other. Multipath not only distorts the data modulated on the carrier, but also the phase of the carrier itself.

Pahlavan & al. (1998) present three signal reception conditions, which may occur at multipath environment

Dominant direct path (DDP) case

Non-dominant direct path (NDDP) case

Undetected direct path (UDP) case

In dominant direct path condition (DDP) the direct line of sight (DLOS) path is detected by the measurement system and it is the strongest path in the channel profile.

In this condition the receiver can lock onto the LOS path and detect its TOA accurately.

In non-dominant direct path (NDDP) condition, the LOS path is detected by the measurement system but it is not the dominant path in the channel profile. Many systems, like traditional GPS receivers, lock onto the strongest path and make an erroneous decision on the time of arrival in NDDP condition. The amount of error made is the time difference between the TOA of the strongest path and the TOA of the

LOS path. To avoid the wrong decision in NDDP condition, more complex receiver architectures have been researched. One example is a RAKE type receiver, which tries to resolve the multipath and make an intelligent decision on the TOA of the DLOS path.

In undetected direct path condition (UDP) the system cannot detect the LOS path, and therefore neither traditional nor RAKE type receivers can detect the LOS path

(Pahlavan & al., 1998).

3.2 Infrared-based Systems

Infrared radiation is electromagnetic radiation of a wavelength longer than visible light. Infrared data transmission has been used for short-range communication among computer peripherals and cellular phones. Infrared is also very popular in remote controls, because the infrared radiation does not penetrate walls and interfere with other infrared controlled devices in adjoining rooms.

26

Infrared has also been used for indoor location finding (Kolodziej & Hjelm, 2006, p.

109). Most of the infrared positioning systems are based on proximity, not ranging.

The location of the mobile device is assumed to be the same as the location of the infrared base station. Active Badge (Want & al., 1992) system uses short-range transmissions of pulse-width modulated infrared light for positioning. A mobile device transmits periodically its identification code, which is received by fixed receivers located at known locations. Multiple infrared transmitters sending their identification code can be positioned throughout the building. Typically, each room in the positioning area has an infrared receiver. The granularity of proximity-based infrared system is low; it is not possible to determine the location of the badge at a higher resolution than the known locations of the base stations (Kolodziej & Hjelm, 2006, p.

109).

The infrared signal can also be used to calculate the spatial position of the target device. These systems utilize computer vision. They consist of infrared emitting tags and a stationary mounted stereo camera. The stereo camera measures the angle of arrival of the emitted light at two different points in space. The spatial positions of the tracked tags are then calculated by triangulation (Aitenbichler & Muhlhauser, 2003).

The infrared light transmission range is only a few meters and it is restricted to the optical line of sight between the transmitter and the receiver. This may be an advantage in the proximity-based systems. The infrared signals received by the base station must be originated from the tags located at the same room. Often the limited range and line of sight requirement is a disadvantage, since the infrared receivers must be deployed to every site separated by walls or other obstructions. The infrared systems also suffer from direct sunlight and high ambient heat. The infrared components are inexpensive, but the cost of deploying the base stations is quite high

(Want & al., 1992).

3.3 Ultrasound

Ultrasound is cyclic sound pressure with a frequency greater 25 kHz, which is the upper limit of human hearing. Ultrasound has many application areas like medical sonography, chemistry and positioning. In positioning applications the ultrasound technology provides great ranging accuracy with low cost electronics.

The range between the ultrasound emitter and listener can be computed from the ultrasound propagation time. The distance is obtained from the following equation

27

d

v us

t

(3.1) where

v us

is the speed of sound and

t

is ultrasound propagation time between the emitter and the transmitter. Because the speed of sound is relative low (344 m/s), the accuracy requirement for measuring the propagation time is also low. For example, only 3 ms timing accuracy is needed to measure the distance within one meter accuracy. In contrast, using radio frequency signal propagating at the speed of light, 3 ns timing accuracy would have been needed.

The speed of sound in air depends on environmental factors like atmospheric pressure, temperature and relative humidity. The variations on the temperature affect most to the speed of sound. In completely dry air the speed of sound depends only on the absolute temperature T (in Kelvin), and is given by Priyantha (2005, p. 62).

v us

20 .

05

T

(3.2)

Priyantha (2005, p. 62) calculates that at room temperature 295 K the speed of sound changes by 0.18% per one K. Temperature change of 10 K would change the ranging measurement by almost 2%. Since the speed of sound has a relative large sensitivity to temperature variations, the ultrasound range measurements should be temperature compensated. In contrast, the speed of sound is not very sensitive to relative humidity and atmospheric pressure variations. For example, a humidity change from 0% to

100% at room temperature and 101.325 kPa pressure changes the speed of sound only by 0.5%. A pressure change from 101.325 kPa to 30 kPa at 298 K and 50% relative humidity would change the speed of sound only by 0.6%. (Priyantha, 2005, p. 63)

Ultrasound signals used in most positioning systems (40-50 kHz) do not penetrate physical objects such as walls. High probability of interference from other ultrasound sources reduces the reliability of the positioning systems based on ultrasound.

Ultrasound-based positioning systems also need an RF link for communications.

3.4 Optical

The location information can be derived from analysis of video images. Vision is a natural way to track the location, because it does not require any special devices attached to the objects to be positioned. However, vision systems typically need huge amounts of processing power to analyze the frames captured. The computer vision

28 systems also have the same line of sight problems as infrared systems, and they may be complex and expensive. They are designed to capture position well in a single room immersive environment. Many of these systems are not designed to be scalable, not even to a building level (Kolodziej & Hjelm, 2006, p. 125-126).

Several research groups have explored using computer vision technology for positioning. Example of such systems is Microsoft Easy Living (Microsoft Research,

2008).

3.5 Inertial Navigation Systems

Inertial navigation systems were first developed for navigating rockets. These systems typically collect information from gyroscopes and accelerometers to determine the position and orientation of the system. Such a system is also called an inertial measurement unit (IMU).

Gyroscopes measure the angular velocity of an object. If the initial orientation of the system is known in the inertial reference frame, the system’s current orientation can be obtained by integrating the angular velocities. There are various types of gyroscopes such as laser gyros, vibrating gyros and mechanical gyros. Also MEMS gyros have been researched during recent years (Saukoski, 2008).

Accelerometers measure the linear acceleration of the system in the inertial reference frame. Because the accelerometers are not aware of their own orientation and they rotate with the system, the accelerations can only be measured relative to the moving system. Orientation relative to the Earth can be measured with 3D accelerometers only

(Wikipedia, [Cited January 24, 2008]).

The linear acceleration of the system in the inertial reference frame can be calculated by measuring both the angular velocity of the system and the linear acceleration of the system, measured relative to the moving system. The position of the system can be obtained by integrating the linear accelerations twice. First, the velocity vector is calculated by integrating the acceleration vector and using the original velocity as initial condition. Second, the position is obtained by integrating the velocity vector obtained from previous step and using the original position as initial condition

(Wikipedia, [Cited January 24, 2008]).

The inertial navigation systems suffer from integration drift. Small errors in measured accelerations and angular velocities cumulate progressively into large errors in position

29 and velocity. Inertial navigation system is often used to complement GPS when the satellite signals are lost or to aid GPS satellite tracking when the user’s dynamics is high (Kaplan, 1996).

Gyroscopes are too expensive for many applications, and accelerators cannot be used alone to determine user’s orientation. In pedestrian navigation a better method to utilize low cost MEMS accelerometers is to detect the steps of the pedestrian. The distance walked or run is obtained by counting the steps and multiplying the step count by estimated step length. A dead reckoning navigation system can be constructed by combining a pedometer with a magnetic compass or gyroscope, which provides the azimuth angle.

Magnetic Compass

A magnetic compass determines the azimuth by sensing the components of the terrestrial magnetic field. Magnetic compass provides absolute direction, while gyroscope provides relative direction to initial direction. However, the magnetic compass suffers from disturbances of magnetic field. Magnets, electric currents or large iron bodies can cause local disturbances, resulting large azimuth errors.

Combining Gyroscope and Magnetic Compass

Gyroscope does not suffer from local magnetic fields, but it needs the initial direction and the direction error grows over time, due to non-constant bias. Thus, the gyroscope needs external input for continuous calibration. A better direction measurement system can be constructed by combining the gyroscope and magnetic compass into a single unit. The gyroscope has excellent short time accuracy, while the magnetic compass provides the absolute direction and long term accuracy. Usually the gyroscope is used as the main source for direction. Magnetic compass provides the initial direction and absolute direction values to compensate the gyroscope drift. Magnetic disturbances can be detected by comparing the gyroscope and magnetic compass output. If the difference in the angular rate exceeds the predetermined threshold, a magnetic disturbance is detected. (Brännström, 2002, p. 11).

3.6 DC Electromagnetic

The direct current (DC) electromagnetic systems generate axial DC magnetic-field pulses from a transmitting antenna in a fixed location. The system measures the position and orientation of one or more receiving antenna sensors with respect to

30 transmitting antenna. The transmitting antenna is driven by a pulsed DC signal. The receiving antenna measures transmitted magnetic field pulse and earth’s magnetic field. (Hightower & Borriello, 2001)

The DC electromagnetic systems are used in the following applications (Ascension

Technology, [Cited January 24, 2008]):

head tracking in flight simulators

head, hand and body tracking for virtual reality environment

body tracking for virtual prototyping and visualization

manipulation of telerobotic controls

 measuring a pilot’s line of sight for aiming weapon systems and interacting with helmet mounted displays

Tracking systems utilizing the DC electromagnetic sensing compute the position and orientation of the receiving antennas by measuring the response in three orthogonal axes to the transmitted field pulse. The constant effect of the earth’s magnetic field is taken into account in the computations. These systems provide 1 mm positioning accuracy and 0.1 degrees orientation accuracy. Disadvantages are the high cost and short range of the signal. The sensors must be located within 1 to 3 meters of the transmitter. Magnetic trackers are designed to capture position in small scale and controlled environment. They are not designed to be scalable for use in large locationaware applications. (Hightower & Borriello, 2001)

31

4 Observables

The classical observables used in radio navigation are:

Received signal strength (RSS)

Angle of arrival (AOA)

Time of arrival (TOA)

Time difference of arrival (TDOA)

Doppler shift

In addition to these metrics the proximity of the target device and base station is used as a positioning observable. These positioning observables are discussed in the following sections.

4.1 Proximity

The simplest positioning method is to use proximity measurements. Knowing only whether or not two devices are in communication range is enough to give a position estimate. In this method the location of the target object is considered to be the location of the base station. If there are several base stations, the strongest base station or the base station serving the target object is selected.

Cell of origin (COO) method used in cellular network positioning is one example of methods based on proximity (Fikouras & al., 2001). In this method the caller’s location is assumed to be the cell’s location serving the mobile phone. Cell of origin is not a very precise location technique. The base station serving the mobile phone is not necessarily the closest base station, and in the urban area the accuracy may be within one hundred meters of the target. On the other hand, the accuracy may be 30 kilometers away from the target where base stations are less densely concentrated.

Because of its inaccuracy, Cell of origin is often used in conjunction with some other positioning technology.

Proximity-based methods can also be applied in local position systems and indoor navigation. Various different signal technologies and sensors are used to detect the proximity of two objects. When radio frequency signals are used, the proximity

32 estimate can be considered as a binary quantization of RSS measurement. Often proximity measurement devices are used in conjunction with distance-based positioning technology. For example, some indoor positioning systems utilize access control ports to monitor the presence of an active RFID tags near the access control ports (Aeroscout, [Cited January 24, 2008]). Also infrared technology is used often in proximity-based positioning systems (Kolodziej & Hjelm, 2006).

4.2 Received Signal Strength

The intensity of a transmitted radio frequency signal decreases as the distance from the transmitter increases. This phenomenon can be used to estimate the location of a mobile device. Measuring the received signal strength (RSS) is quite straightforward and it is done in most of the radio receivers.

In free space the electromagnetic waves obey the inverse square law, which states that the attenuation of the electromagnetic wave is inversely proportional to the square of distance between the transmitter and the receiver. Inverse square law can be also utilized in positioning techniques by converting the measured signal strengths into range estimates. The location of the target object can be computed from these range measurements by using trilateration. Another method to estimate the position of the object by using RSS measurements is the location fingerprinting method, which is

explained in Chapter 5.4.

Figure 1. Relation between received signal strength and distance on the free space model.

33

In the free space model, relation between received signal strength (RSS) and distance is described as (Kitasuka, 2005b)

P r

(

d

)

P

0

20 log

10

4

d

[dBm]

(4.1) where P

0

is empirical constant,

is the wave length of the signal, and d is the distance

between the receiver and transmitter. Figure 1 illustrates the relation between received

signal strength and distance on free space model.

When at least two RSS measurements are available, a two-dimensional position of the object can be solved by using trilateration. The RSS measurements form circles around base stations, where the radii of the circles correspond to the estimated signal strengths. The position estimation using three received signal strength measurements is

illustrated in Figure 2.

Figure 2. Intersection of three circles

The radio signal propagation is not only affected by the inverse square model, but also by a number of other factors determined by its path from the transmitter to the receiver. For example the air temperature and pressure affect to the signal attenuation as well as any obstructions on the signal path. Also the multipath propagation affects to the received signal strength. In indoor positioning systems, the position of the target object is seldom computed by using geometric range estimates and trilateration.

Instead, pattern matching algorithms, such as location fingerprinting, are used more often.

34

4.3 Angle of Arrival

Angle of arrival method (AOA) is sometimes also referred as direction of arrival

(DOA) method. Direction finding systems utilize antenna arrays and try to estimate the angle of arrival of the signal of interest. The estimated AOA restricts the transmitter location along line in the estimated AOA. When several AOA estimates are available from multiple base stations at different locations, the position of the transmitter can be

located at the intersection of these lines. Figure 3 illustrates two dimensional

positioning using AOA measurements of three base stations. While only two AOA measurements are needed to estimate the location of the transmitter, multiple AOA measurements can be used to improve the estimation accuracy.

Figure 3. Location solution derived from angle of arrival measurements

Usually the angle of arrival (AOA) is measured at a base station. The hardware of the antenna measuring the AOA of the signal is relative complex. In addition, the antenna has to be precisely calibrated to the correct orientation. Measuring the direction of the arrived signal is seldom practical at a mobile terminal, because it is difficult to keep the antenna in the proper attitude in a moving object. Thus, AOA positioning systems are usually network-centric. This means that the AOA measurements made by the base stations are collected to a network server, where the position estimation takes place.

Positioning systems based on AOA measurements have some advantages with respect to the other techniques. For example, in TOA and TDOA techniques the clocks of the base stations have to be accurately synchronized to a common time reference. In AOA systems the complex time synchronization is not needed. In AOA method, twodimensional position estimate can be calculated by using AOA measurements of two

35 base stations only. In contrast, the TDOA method needs measurements of at least four base stations. TOA method would need measurements from three base stations if twoway communication (two-way ranging) is used, and four measurements if one-way ranging is used.

Among disadvantages, the accuracy of the position estimate calculated from the AOA measurements diminishes with increasing distance between the transmitter and the receiver. In addition, AOA performs worse than TOA/TDOA methods in indoor environment due to multipath propagation (Pahlavan & al., 2002). AOA estimation techniques estimate the direction of a transmitter based on the strongest received signal, which is assumed to be the LOS signal (Mizusawa, 1996). However, the NLOS condition, where the direct LOS path is obstructed, is quite common in indoor environment. In NLOS condition, the AOA estimate will be the direction of the strongest multipath component, which causes errors to the AOA estimate. Also in nondominant direct path (NDDP) case, where LOS signal is available but not the strongest signal, multipath causes significant errors to the AOA estimation. Antenna arrays are also relative complex and thus quite expensive.

Due to the multipath problems, AOA technique is seldom used in indoor positioning systems. However, AOA is sometimes used in outdoor local positioning systems as a complementary measurement to TOA or TDOA measurements.

The AOA can be measured by using various techniques. The following AOA estimation techniques are explained in the following sections:

Measuring the direction having the strongest signal strength

Measuring the phase difference between two or more antenna elements

Measuring the Doppler shift from a rotating antenna

4.3.1 Signal Strength Direction Finding

In the direction finding based on the signal strength, the AOA is found by rotating the antenna. The source of the transmission is determined from the direction where the maximum signal level is obtained. Another technique is to arrange an array of antennas in circular form and measure the signal strength from all of the antennas. The AOA estimate is obtained from the direction which has the strongest signal.

36

The signal strength direction finding does not provide very accurate results. To measure the angle of arrival with a better accuracy requires much more sophisticated techniques, like phase difference direction finding described in the next section.

4.3.2 Phase Difference Direction Finding

The angle of arrival can be measured by an accuracy of less than one degree by measuring the phase difference between two or more antenna elements. This method is also called short baseline interferometry or phase interferometry.

In this method the phase of the signal is measured in two antennas separated by a distance d. If a wavefront arrives to the antennas at an angle θ, the extra path between the two antennas causes a phase difference,

, between the two antennas. The angle of arrival can be calculated from the phase difference using the equation:

(

)

 arcsin

 

2

d

(4.2) where

is the wavelength of the signal. For unambiguous results, the antennas must be spaced half a wavelength apart, or less.

The severe multipath condition of the indoor environment causes substantial errors in phase measurements and thus to the AOA estimated by using phase difference direction finding.

4.3.3 Doppler Direction Finding

The Doppler technique is a direction finding method that produces an estimate on the received signal by measuring the Doppler shift induced on the signal by sampling around the elements of a circular array.

The original method used a single antenna that physically rotated. When a rotating antenna moves towards the wavefront, a maximum frequency occurs. When the antenna moves to the same direction as the wavefront, a minimum frequency occurs.

The direction of the wavefront is obtained from the antenna attitude where the Doppler shift is getting smaller and approaching to zero. The modern approach uses a multiantenna circular array with each antenna sampled in succession.

37

4.4 Time of Arrival

Many navigation systems like GPS utilize the concept of time of arrival (TOA) ranging to determine user position. This concept is based on measuring the time it takes for a signal transmitted by an emitter to reach a receiver. This time interval, referred to as the signal propagation delay, is then multiplied by the speed of the signal to obtain the distance between the emitter and the receiver. The signal may be an electromagnetic wave, audible sound or ultrasound signal. The position of the target object can be determined by measuring the propagation times from multiple base stations at known locations.

In network centric systems the user terminal emits the signal and the base stations receive the signal. In user centric systems, like GPS, the roles of the emitter and receiver are opposite.

This chapter explains the time of arrival estimation process using radio signal

(electromagnetic wave). However, the TOA positioning algorithms explained in

section 5.1 can be used with ultrasound measurements as well. There are two major

challenges when estimating the TOA of the radio signal: the multipath phenomenon distorts the TOA measurements especially at indoor environment, and the base stations of the positioning system must be synchronized accurately in time. These challenges are illustrated in the subsequent sections.

4.4.1 Measuring Time of Arrival at Multipath Conditions

The arrival time of the signal is measured in the positioning systems which are based on measuring the signal propagation delay or time difference of arrival. The arrival time of the signal can be measured by measuring the phase of received narrowband carrier signal or directly measuring the arrival time of a wideband narrow pulse. In wideband systems the pulses can be generated directly or using spread spectrum technology (Pahlavan & al., 2002).

Narrowband Signals

In the narrowband systems, the distance between two points is obtained by measuring the phase difference between the received and the transmitted carrier signals. The TOA

 of the signal is calculated as (Pahlavan & al., 2002)

38

2

f c

(4.3) where

is the measured phase of the carrier signal and the distance between the transmitter and the receiver is longer than the wave length of the carrier signal, there are also multiple integer carrier waves between the two points.

The number of integer carrier waves can be obtained from lower frequency signal such as data modulated on the carrier signal.

The severe multipath condition of the indoor environment causes substantial errors in phase measurements. In multipath environment, the composite received carrier signal is the sum of a LOS and multiple reflected carriers, all arriving along different paths.

The components of the received signal have the same frequency but different phase and amplitude. The frequency of the composite received signal remains unchanged, but the phase will be different from the phase of the directly arriving signal. The composite signal contains no information from which the correct TOA or distance could be estimated. Pahlavan & al. (2002) conclude that phase-based distance measurement using narrowband carrier signal cannot provide accurate distance estimate in a heavy multipath environment.

Wideband signals

Many navigation systems transmitting wideband signals utilize direct sequence spread spectrum (DSSS) modulation. The most well known example of these systems is GPS.

The DSSS wideband signals are used also in most of the current WLAN communication systems. The same principles as used in GPS can be used in ranging in local positioning systems also.

In GPS satellite navigation system the DSSS provides the structure for the transmission of ranging signals and navigation data (Kaplan, 1996, p. 39). The ranging signals are coded by a known pseudo random number (PRN) code and transmitted by a transmitter. Then a receiver cross correlates received signal with a locally generated replica PRN code sequence using a sliding correlator. The distance between the transmitter and the receiver is obtained from the arrival time of the first correlation

peak. The correlation process is illustrated in Figure 4.

39

Figure 4. Correlation of DSSS signal

A specific code phase generated by the satellite at t

0

arrives at the receiver at t

1

. The signal propagation time is

t

t

1

t

0

. At the receiver an identical replica code is generated at t

0

, with respect to the receiver’s own clock. This replica code is shifted in time until it achieves correlation with the satellite generated ranging code. If the receiver clock is synchronized with the transmitter perfectly, the correlation process would yield the true propagation time, and the distance between the transmitter and the receiver is obtained by multiplying this propagation time by the speed of light (Kaplan,

1996, p. 41).

However, the transmitter and receiver clocks are generally not synchronized. In this case the propagation time measurement includes the receiver clock offset from the system time and the measurement is denoted as pseudorange. Another way of utilizing the correlation process described above is to measure the time difference of arrival of two signals from different transmitters with respect to receiver’s inaccurate clock. The time difference of arrival measurement is then used in hyperbolic positioning.

Wideband DSSS signal is commonly used for the TOA-based ranging systems because

of several advantages as compared with other alternatives. From Figure 5 we observe

that the timing accuracy improves as the bandwidth increases (Szewczyk, 2004). In addition, the large bandwidth of the DSSS signal helps to resolve multipath signals.

The correlation process also provides processing gain to the receivers of the DSSS systems. This processing gain makes DSSS ranging systems perform much better than other systems in suppressing interference from other radio systems operating in the same frequency band (Li, 2003, p. 60).

40

Figure 5. Approximate range resolution versus signal bandwidth (Szewczyk, 2004)

In single-path radio propagation channels the variance of the time of arrival estimate is bounded by the Cramer-Rao Lower Bound (CRLB). In practice this means that the accuracy of the time of arrival estimate can be only a small fraction of the wavelength of the signal. For example, in GPS the C/A-code noise at the receiver is on the order of

1.5 m while the wave length of the C/A code is about 300 m (Kaplan, 1996, p. 256).

However, the multipath radio propagation channel is very complex, and the CRLB is not directly applicable to multipath environment. Pahlavan & al. (2002) state that in multipath environment the resolution of TOA estimation in DSSS is roughly determined by the signal bandwidth. Also, according to Dumont & al. (1994) the resolution of multipath delays using the correlation method is limited by the chip interval of the PRN sequence, and the range errors normally range from zero to half of the chip interval. For example, if the PRN sequence is transmitted at a rate of 10 MHz, the resolution of multipath arrivals is limited to 30 m, and the errors range from 0 m to

15 m.

If the same correlator designed for the single-path channels is used in the multipath channel, the TOA estimation accuracy degrades significantly. Li (2003, p. 63) presents the following general principles, which can be used to improve the TOA estimation in multipath channels:

Increase the receiver sensitivity and the receiver dynamic range

Improve the resolution of estimation techniques by increasing the signal bandwidth

41

Improve the resolution of estimation techniques by employing advanced signal processing techniques

Place the transmitter and receiver in a way to minimize the occurrence of the

NLOS propagation scenarios between the transmitter and the receiver.

Improving the TOA estimation by increasing the signal bandwidth is not always possible due to the scarcity of the available bandwidth. On the other hand, it is always desirable to improve ranging accuracy using the same bandwidth. The accuracy of the

TOA estimation can be improved by employing super-resolution techniques. The super-resolution techniques have been studied in the field of the model-based parameter spectral estimation for a variety of applications (Li, 2003, p. 82).

The super-resolution techniques are used to improve the ranging resolution beyond that what is possible with a traditional PRN correlation technique. Li (2003) and

Dumont & al. (1994) have studied performance of Multiple Signal Classification

(MUSIC) super-resolution algorithm in multipath conditions. MUSIC algorithm uses an eigenvector-based approach to model the data, and it is described in detail by Li

(2003, p. 83).

Pahlavan & al. (2002) have compared the performance of MUSIC algorithm to inverse

Fourier transform (IFT) method and to traditional cross correlation with DSSS signals

(DSSS/xcorr). The simulation results of the comparison are shown in Figure 6.

Figure 6. Estimated TOA of the DLOS path and normalized time domain responses obtained using three different techniques. The vertical dash-dot line denotes the expected TOA. The x-axis delay in ns. (Pahlavan & al., 2002)

42

From Figure 6 it is observed that the MUSIC algorithm has much higher time domain

resolution than the other two methods. The MUSIC algorithm detects the expected

TOA accurately while the other two methods fail. According to Pahlavan & al., the

MUSIC method is preferred, especially when the signal bandwidth is small. However, it should be noted that even super-resolution techniques could not eliminate large ranging errors at some locations because of NLOS conditions between transmitter and receiver. Faulty measurements should be identified and removed in the position calculation process. It is worth mentioning that while the super-resolution techniques can improve the TOA estimation performance, they also increase the complexity of system implementation (Pahlavan & al., 2002).

Ultra-wideband Signals

As mentioned in the previous chapter, the accuracy of the TOA estimation can be improved by increasing the signal bandwidth. UWB systems, whose bandwidth is typically more than 1 GHz, have been researched in order to develop indoor positioning systems with high TOA estimation accuracy. It has been shown that the

UWB signal does not suffer from multipath fading, which is desirable for accurate

TOA estimation in indoor environment (Ramirez-Mireles, 2001).

The use of ultra-wideband signals in indoor navigation is explained in more detail in

section 7.3.

4.4.2 TOA Measurement Methods

The techniques to estimate the time of arrival were discussed in the previous sections.

To convert the time of arrival estimation to the signal propagation delay estimation for the purpose of ranging, the spatially separated transmitter and the receiver have to be synchronized in time. Because the electromagnetic wave propagates at the speed of light, only a one microsecond error in timing would cause a 300 meter error in range.

If the requirement for the distance measurement accuracy is 3 meters, the time synchronization has to be made with 10 nanoseconds accuracy. Synchronizing the base station clocks and measuring the time of arrival with 10 nanoseconds accuracy is a challenging task.

43

The following methods to form the propagation delay estimate are discussed in this section:

Synchronized transceiver method (one-way ranging)

Pseudorange method (one-way ranging)

Round-trip TOA method (two-way ranging)

Symmetric double-sided two-way ranging

In addition to these methods, the arrival time measurements can be used to form a time

difference of arrival (TDOA) estimate. The TDOA method is explained in section 4.5.

Synchronized Transceiver Method

In the synchronized transceiver method the transmitter and the receiver are accurately synchronized in time. Also the transmission time has to be known. The signal

propagation delay estimation process is shown in Figure 7.

Figure 7. Signal propagation delay estimation in one-way ranging

The terminal A sends a radio signal to terminal B at time t

0

. Then the terminal B receives the signal and measures the arrival time t

1

. The signal propagation delay

t

is obtained by subtracting the known transmission time t

0

from the measured arrival time

t

1

. The range between the terminals A and B is obtained from

r

AB

c

 

t

(4.4) where c is the speed of light and

t

t

1

t

0

. If there are at least three terminals located at known positions, three dimensional position of a mobile terminal (x

u

, y u

, z u

) can be obtained from the following set of equations

44

r i

(

x i

x u

)

2

(

y i

y u

)

2

(

z i

z u

)

2

(4.5) where r is the range estimate of the ith base station and (x

i i

, y i

, z i

) denote the ith base station position in three dimensions. The position computation algorithms to solve

Equation (4.5) are explained in 5.1.

Pseudorange Method

In all timing-based navigation systems the clocks of the base stations are accurately synchronized in time with each other. The base stations may have atomic clocks or

GPS time reference, or the known distances between the fixed base stations are utilized in the time synchronization process. However, synchronizing the clock of the mobile terminal to the common time reference is a challenging task. Typically the base station implementation can be relative expensive, but the price of the mobile terminal must be very low. Use of accurate time references in the mobile devices is usually impossible due to their high price. Many systems like GPS allow the use of a low cost time reference in the user terminal.

The receivers of these systems measure the arrival time of the radio signal with an inaccurate clock, whose time is biased from the common system time. The positioning observable can be formed from these biased arrival time measurements in two ways:

A pseudo-propagation delay estimate is formed by subtracting the known transmit time from the biased arrival time

A time difference of arrival (TDOA) estimate is formed by subtracting two arrival time measurements from each other. The transmit time of the signal need not be known.

The first method is called pseudorange method in this thesis. The TDOA method is

explained in section 4.5.

The pseudo-propagation delay consists of the geometric range time equivalent and the unknown time offset t

u

between the mobile terminal clock and the system time. The pseudorange

AB

between the terminals A and B is

AB

r

AB ct u

(4.6)

45 where r is the geometric range between the terminals A and B. The clock offset t

u

is the same for all measurements. The unknown time offset adds one unknown variable to the positioning equations. Thus, one additional measurement is needed if the mobile terminal clock is not synchronized to the base station clocks.

If there are at least four terminals located at known positions, three-dimensional position of a mobile terminal (x

u

, y u

, z u

) and the time offset t

u

can be obtained from the following set of equations

i

(

x i

x u

)

2

(

y i

y u

)

2

(

z i

z u

)

2

ct u

(4.7) where

is the range estimate of the ith base station and (x

i i

, y i

, z i

) denote the ith base station position in three dimensions. The position computation algorithms to solve

Equation (4.7) are explained in section 5.1.

Two-way Ranging Method

In synchronized transceiver method the remotely located transmitter and receiver must be synchronized to a common time reference. To avoid the time synchronization requirement, the two-way ranging method can be employed to measure the signal propagation delay.

Figure 8. Two-way ranging (Hach, 2005)

Two-way ranging technique does not require exact time synchronization between the clocks of the originator and recipient devices. On the other hand, two way communications is needed. A range between two devices A and B is determined via

46 two-way exchange of a message and measuring its arrival time. This method known also as two-way time-of-arrival (TW-TOA) or Round Trip Time (RTT) is illustrated in

Figure 8 (Hach, 2005).

The range between the devices A and B is measured as follows. First the originator device A sends a ranging message D1 and records the transmitting time t

a1

with respect of clock A. Then the recipient device B receives the message and records the receiving time t

b1

with respect of clock B. Next the recipient B sends an acknowledgement message A1 and records the transmission time t

b2

. Finally, the originator A receives the acknowledgement message and records the receiving time t

a2

. The elapsed time between the departure of ranging message from A, and the reception of the acknowledgement at A can be approximated as

T roundA

2

T t

T replyB

(4.8) where T

t

is the one way time-of-flight of the ranging message and T

replyB

= (t

b2-

t b1

) is the reply time at the recipient device B. Usually the recipient transmits the reply time

T replyB

or t

b1

and t

b2

to the originator in a separate message after the ranging message exchange (802.15.4a). The time-of-flight of the ranging message is solved from the following equation

T t

T roundA

T replyB

2

(4.9)

The range between the devices A and B is

r

AB

T t

c

(4.10) where c is the speed of light.

Usually the reply time T

replyB

is much larger than the time-of-flight T

t

. Thus the round trip time T

roundA

and the reply time T

replyB

will be almost equal. Because the reply time is relative long, the oscillators in both of the devices should be very stable. In other words, drift in the oscillator A or B would cause significant error to the time-of-flight measurement.

Hach (2005) gives an example. Let the signal propagation time T

t

be 30 ns and reply time T

replyB

be 1.000000 ms. Then the round trip time T

roundA

will be 1.000060 ms.

47

Assume that clock A drifts +10 ppm and clock B drifts -10 ppm. Then the time-offlight will be

T t

T roundA

T replyB

2

1 .

000060

10

3 s

( 1

10

6

)

1 .

000060

10

3 s

( 1

10

6

) s

40

9 s

40 ns

2

(4.11)

This example shows that clock accuracy of order 10 ppm may cause 10 ns error to the time-of-flight measurement.

Symmetric Double Sided Two-way Ranging

Although the time synchronization between the devices is not needed in the two-way ranging, the clock drift of the devices is still a problem. This is because the reply time

T replyB

is a rather long interval compared with the time-of-flight of the signal. The error due to the clock drift can be eliminated by making the two-way ranging measurement transaction two times (Hach, 2005). The first ranging measurement is calculated based on a round trip from device A to device B and back to device A (like in two-way ranging). The second measurement is calculated based on a round trip from device B to device A and back to device B. This method, called Symmetric Double Sided Two-way

Ranging is illustrated in Figure 9 (Hach, 2005).

From Figure 9 it is obtained that time of flight of the ranging message is (Hach, 2005)

T t

T roundA

T replyB

T roundB

T replyA

4

(4.12)

T roundA

and T

replyA

are measured with the oscillator of the device A. Both of these measurements are biased by the oscillator offset

T

A

of the device A. Similarly T

roundB

and T

replyB

are biased by the oscillator offset

T

B

of the device B. Symmetric double sided two-way ranging protocol cancels the oscillator offsets (Hach, 2005):

d

c

T

Round

,

A

T

Reply

,

B

c

T

A

T

B

4

T

B

T

Round

,

B

4

T

A

0

T

Reply

,

A

(4.13)

48

Figure 9. Symmetric double sided two-way ranging (Hach, 2005)

According to Hach (2005) the symmetric double sided two ranging can work with up to 40 ppm crystal tolerances, while better than 10 ppm crystal tolerances are needed in the straightforward version of the two-way ranging.

4.5 TDOA Measurement Methods

The most common method to convert the time of arrival estimation to the signal propagation delay estimation is the time difference of arrival (TDOA) method. In this method the difference of arrival times of two signals is measured. Like in TOA methods, the base stations must be synchronized in time, but the mobile terminal does not need to be time synchronized. In contrast to TOA method, the transmit time of the signal is not needed in TDOA method. In TDOA method the position of the mobile

terminal is obtained from intersection of multiple hyperbolas (Figure 10), while in the

TOA method the position of the mobile terminal is determined by intersection of multiple spheres.

49

Figure 10. Intersection of two hyperbolas

In principle the time difference of arrival of two signals can be measured on the mobile terminal. However, most local positioning systems are designed so that the mobile terminal transmits the signal, whose arrival time is then measured at multiple base stations with respect to a common time reference. The arrival times are then sent to the server computer, which computes the TDOA estimates by subtracting the arrival time of a pivot base station t :

i t

TDOA

,

i

t i

t

1

(4.14) where i ranges from 2 to N. Thus, N-1 TDOA estimates are obtained from N arrival time measurements.

In addition to obtain TDOA estimate from the difference of two arrival times, the

TDOA estimate can be obtained by using cross correlation technique. In this technique the received signal at one base station is correlated with the received signal at another base station. The TDOA estimation using cross correlation technique is illustrated in

Figure 11 (Aatique, 1997).

Figure 11. Cross correlation method for TDOA estimation (Aatique, 1997)

50

In Figure 11 signals x

1

(t) and x

2

(t) are first filtered by H

1

(f) and H

2

(f), then correlated, integrated and squared. This is performed by incrementing the value of the time delay by small steps, until a peak correlation is obtained. The time delay causing the cross correlation peak is an estimate of the TDOA.

51

5 Location Estimation Algorithms

This chapter discusses the location estimation processes using AOA, TOA, TDOA and

RSS observables.

5.1 Time of Arrival Techniques

The time of arrival technique exploits trilateration to determine the position of the mobile users. Position estimation by the trilateration is based on knowing the range from the mobile unit to at least three (3D) base stations at known locations. The range is determined from the propagation delay of the signal or received signal strength

indicator (RSSI). Two-way ranging method illustrated in section 4.4.2 provides a range

estimate without the need to synchronize the base station or mobile device clocks.

In the positioning systems measuring the range, the position is estimated by intersecting circles (2D) or spheres (3D) with radius r

i

and centre (x

i

, y i

, z i

), as

illustrated in Figure 12. Radius of the circle r

i

is obtained from the propagation delay of the signal or received signal strength indicator (RSS). Point (x

i

, y i

, z i

) is the known location of the base station transmitting or receiving the signal.

Figure 12. Trilateration by using three measurements

52

When distance measurements r

i

are available from at least three base stations, the three-dimensional location of the receiver (x

u

, y u

, z u

) can be solved from the following set of non-linear equations (Kaplan, 1996, p. 44):

r i

(

x i

x u

)

2

(

y i

y u

)

2

(

z i

z u

)

2

f

(

x u

,

y u

,

z u

)

(5.1) where i ranges from 1 to 3 and references the base stations at known locations, (x

i

, y i

, z i

) denote the ith base station coordinates in three dimensions, and r

i

is the range measurement from ith base station.

In one way ranging the base station clocks must be synchronized with each other. In these systems the time-of-flight of the signal is obtained by subtracting the transmit time from the measured time of arrival of the signal. The transmit time is usually obtained from the data of the signal used for the time of arrival estimation. If the transmit time is not known, the time difference of arrival (TDOA) technique must be used.

Usually in one-way ranging systems the clock of the mobile device is not synchronized with the base station clocks. Thus the range derived from the signal propagation time is denoted as the pseudorange. The pseudorange consists of a geometric range between the transmitter and the receiver, and an offset between mobile device clock and base station clock. This clock offset adds one unknown variable to the positioning equations. Thus, one additional measurement is needed if the mobile terminal clock is not synchronized to the base station clocks.

In order to determine the user position in three dimensions (x

u

, y u

, z u

) and the user clock offset t

u

, four pseudorange measurements are needed. The user position can be solved from the set of equations (Kaplan, 1996, p. 44)

i

(

x i

x u

)

2

(

y i

y u

)

2

(

z i

z u

)

2

ct u

f

(

x u

,

y u

,

z u

,

t u

)

(5.2) where i ranges from 1 to 4 and references the base stations at known locations, and (x

i

, y i

, z i

) denote the ith base station coordinates in three dimensions, and t

u

represents the advance of the satellite clock with respect to the system time, and

 is the measured

i

pseudorange to the ith base station.

The nonlinear equations (5.1) or (5.2) can be solved for the unknowns by using either

closed form solutions, iterative methods based on linearization, or Kalman filtering.

53

The linearization of the range equations (5.2) using Taylor series expansion is

presented in (Kaplan, 1996, p. 44-46). Using an approximate position location

(

x

ˆ

u

,

u

,

z

ˆ

u

) and time bias estimate

tˆ

u

an approximate position can be calculated

(5.3)

i

(

x i

u

)

2

(

y i

u

)

2

(

z i

z

ˆ

u

)

2

c t

ˆ

u

f

(

x

ˆ

u

,

u

,

z

ˆ

u

,

t

ˆ

u

)

The relationship between the unknown position, the approximate position and the displacement between them is (Kaplan, 1996, p. 44):

x u

y u

z u

z

ˆ

ˆ

u y

ˆ

u u

x u

x u

 

z u t u

t

ˆ

u

 

t u

(5.4)

Therefore, we can write

f

(

x u

,

y u

,

z u

,

t u

)

f

(

x

ˆ

u

 

x u

,

 

y u

,

z

ˆ

u

 

z u

,

t

ˆ

u

 

t u

)

(5.5)

This function can be expanded about the approximate point and associated predicted clock offset (

x

ˆ

u

,

u

,

z

ˆ

u

,

t

ˆ

y

) using a Taylor series (Kaplan, 1996, p. 45):

f

(

u

f

x u

,

(

u

,

u

,

x

ˆ

u

 

y u

,

z

ˆ

u

,

t

ˆ

u

)

z

ˆ

u

f

(

u

,

u

,

z

ˆ

u z

ˆ

u

,

t

ˆ

u

)

x u

z u

z u

f

,

t

ˆ

u f

 

t u

)

f

(

(

u

,

u

y u

,

z

ˆ

u

,

t

ˆ

u

(

x

ˆ

u

,

y

ˆ

u

t

ˆ

u

,

z

ˆ

u

,

t

ˆ

u

)

)

x

ˆ

u

t

,

y u u y

ˆ

u

,

z

ˆ

u

,

t

ˆ

u

)

...

(5.6)

The expansion has been truncated after the first order partial derivatives to eliminate non-linear terms. Taking partial derivatives and substituting (5.3) into (5.6) yields

(Kaplan, 1996, p. 45)

i

i

i

x i

x

ˆ

u r

ˆ

i

x u

y i

y

ˆ

u r

ˆ

i

y u

z i

z

ˆ

u r

ˆ

i

z u

c

t

ˆ

u

(5.7) where and

(

u

,

u

,

z

ˆ

u

) is an approximate position location, and

ˆ is an approximate pseudorange, and

i

(

x u

,

y u

,

z u

,

tˆ

u

is a time bias estimate

t u

) is the displacement from the approximate position to the true position, and

54

r

ˆ

i

(

x i

u

)

2

(

y i

u

)

2

(

z i

z

ˆ

u

)

2

(5.8)

The above equation is simplified by introducing new variables where (Kaplan, 1996, p.

46)

i a xi a yi a zi

ˆ

i

i z x y i i i

r

ˆ

i

y

ˆ

u

r

ˆ

i z

ˆ

u r

ˆ

i x

ˆ

u

(5.9)

Equation (5.7) can be written more simply as (Kaplan, 1996, p. 46)

i

a xi

x u

a yi

y u

a zi

z u

c

t u

(5.10)

In Equation (5.10) there are four unknowns

(

x u

,

y u

,

z u

,

t u

) which can be solved by making ranging measurements to four base stations. The unknown displacement

can be determined from Equation (5.11) shown in matrix form

Δρ

H

x

(5.11) where

x

x u

y u

z u c

t u

H

a

a

a a x x

1

x

3

x

2

4

a y

1

a y

2

a y

3

a y

4

a z

1

a z

2

a z

3

a z

4

1

1

1

1

ρ

1

2

3

4

(5.12)

In Equation (5.12)

i

is the difference of approximate (predicted) and measured pseudorange.

Equation (5.12) has the solution

x

H

1

Δρ

(5.13)

55 where H is the direction cosine matrix containing unit vectors pointing from the linearization point to the location of the ith base station,

x

is the offset of the user from the linearization point, and

ρ

contains differences between the predicted and observed ranges (Kaplan, 1996, p. 47).

User’s position

(

x u

,

y u

,

z u

) and the clock offset

t u

are calculated by using an iterative process. At the system startup the approximated location and time offset

(

x

ˆ

u

,

u

,

z

ˆ

u

,

t

ˆ

y

) are set to some initial value. In case of local positioning systems, the center of the service area is usually a good choice. Next the direction cosine matrix H and the predicted-minus-observed pseudorange vector

ρ

are computed. After that, the unknown displacement vector (

x u

,

y u

,

z u

,

t u

) is calculated by using Equation

(5.13). A new approximate position and clock offset are obtained by using Equation

(5.4). The iteration process is repeated until the length of the displacement vector does

not become smaller any more.

Least Square Solution

In reality, the range or pseudorange estimates are corrupted by errors, such as measurement noise and multipath. These measurements errors translate to errors in the components of vector

x

, as presented below (Kaplan, 1996, p. 47)

ε

x

H

1

ε

meas

(5.14) where

ε

meas

is the vector containing range or pseudorange measurement errors and is the vector representing errors in the user position and receiver clock offset.

The error contribution

ε can be minimized by using measurements of more base

x

stations than the number of unknown variables in the computation. Using redundant measurements will result an over-determined solution, which can be solved by employing least squares estimation techniques.

The residual vector r is defined (Kaplan, 1996, p. 520)

r

H

x

 

ρ

(5.15)

The ordinary least squares solution is defined as the value of

x

that minimizes the square of the residual. This is equivalent to minimizing the sum of squares of the components in the residual vector. The square of the residual R

SE

is given as a function of

x

(Kaplan, 1996, p. 520).

56

R

SE

(

x

)

(

H

x

 

ρ

)

2

The following solution minimizes the residual R

SE

(Kaplan, 1996, p. 520)

x

(H

T

H)

1

H

T

Δρ

(5.16)

(5.17)

5.2 Time Difference of Arrival Positioning Techniques

In hyperbolic positioning method the location of an object is calculated from the time difference of arrival (TDOA) measurements. The position of the object is determined from the intersection of a set of hyperbolas defined by the TDOA estimates, as

illustrated in Figure 13.

Distance between the mobile terminal and ith base station is

r i

(

x i

x u

)

2

(

y i

y u

)

2

(

z i

z u

)

2

(5.18) where i is the index of the base station, (

x u

,

y u

,

z u

) is the unknown position of the mobile terminal and (x

i

, y i

, z i

) is the known location of the ith base station. The distance difference between the mobile terminal with respect to the base station where the signal arrives first, is (Aatique, 1997, p. 23).

r i

, 1

cd i

, 1

r i

r

1

(

x i

x u

)

2

f

(

x u

,

y u

,

z u

)

(

y i

y u

)

2

(

z i

z u

)

2

(

x

1

x u

)

2

(

y

1

y u

)

2

(

z

1

z u

)

2

(5.19) where c is the signal propagation speed, r

i,1

is the distance difference between the first base station and the ith base station, r

1

is the distance between the first base station and the mobile terminal, and d

i,1

is the measured TDOA between the first base station and

the ith base station. Equation (5.19) defines a set of nonlinear hyperbolic equations

whose solution provides the three-dimensional coordinates of the mobile terminal.

57

Figure 13. Hyperbolic positioning

Solving the set of equations (5.19) using Taylor series expansion is presented in the

following subsection. In the literature, there are also several other methods to solve the hyperbolic equations. These methods are discussed later on this chapter.

5.2.1 Taylor Series Method for Hyperbolic Equations

The Taylor series method used with the TDOA measurements is almost the same as the Taylor series method used with the TOA measurements. In GPS calculation (TOA) the Taylor series method has been proven accurate and robust.

The linearization process presented in (Kaplan, 1996, p. 44-47) for TOA measurements is applied here for TDOA measurements. The concept of the approximate position is used again. The unknown user position is considered to consist of an approximate component and an incremental component as stated below

x u

y u

z u

ˆ

u

 

x u z

ˆ

y

ˆ

u u

 

x u

z u

(5.20)

Therefore we can write (Kaplan, 1996, p. 45)

f

(

x u

,

y u

,

z u

)

f

(

x

ˆ

u

 

x u

,

 

y u

,

z

ˆ

u

 

z u

)

(5.21)

The time difference of arrival d

i,1

can be expanded about the approximate point

(

x

ˆ

u

,

y

ˆ

u

,

z

ˆ

u

)

using Taylor series as presented in Equation (5.22).

58

f

(

x

ˆ

u

 

x u

,

y u

,

f

(

z

ˆ

u

 

z u

)

u

,

y

ˆ

u u

,

z

ˆ

u

)

y u f

(

f u

,

y

ˆ

u

(

,

z

ˆ

u

)

f

(

,

y

ˆ

u

z

ˆ

u u

,

z

ˆ

u

)

z u u

,

u u

,

z

ˆ

u

)

x u

...

(5.22)

Like with the TOA calculation, only the first order partial derivatives are taken into account. Taking partial derivatives yields

r i

, 1

r i

, 1

(

r

ˆ

i

r

ˆ

1

)



x

1

r

ˆ

1

ˆ

u

x i

r

ˆ

i

ˆ

u



x u



y

1

r

ˆ

1

y

ˆ

u

y i

r

ˆ

i

ˆ

u

y u



z

1

z

ˆ

u r

ˆ

1

z i

z

ˆ

u r

ˆ

i



z u

(5.23) where

r i

ˆ

(

x i

x

ˆ

u

)

2

(

y i

y

ˆ

u

)

2

(

z i

z

ˆ

u

)

2

(5.24)

Equation 5.23 is simplified by introducing new variables where

r i

, 1

a a a xi yi zi

r i

, 1

(

r

ˆ

i

r

ˆ

1

)

x z y

1

1

1

x

ˆ

u r

ˆ

1

ˆ

u r

ˆ

1

z

ˆ

u r

ˆ

1

z x i

y i r

ˆ

i

x

ˆ

u u i

r

ˆ

i z

ˆ

u r

ˆ

i

(5.25)

Equation 5.23 can be written more simply as

r i

, 1

r i

, 1

(

r i

ˆ

r

ˆ

1

)

a xi

x u

a yi

y u

a zi

z u

(5.26)

In the Equation (5.26) there are now three unknowns

(

x u

,

y u

,

z u

) which can be solved for by using three pair of TDOA measurements. In other words, measurements of four base stations are needed. The unknown displacement can be determined by solving the set of linear equations below shown in matrix form

59

Δr

G

x

(5.27) where

x

x

y

z u u u

G

a

a a x

2

x

3

x

4

a y

2

a y

3

a y

4

a a z a z

3

z

2

4

r

r

2 , 1

r r

3 , 1

4 , 1

(5.28)

In equations (5.27) and (5.28)

x

is the offset of the user from the linearization point, and

r

contains the difference between the measured TDOA and predicted TDOA.

Like with the TOA method, In TDOA method the user’s position

(

x u

,

y u

,

z u

) is calculated by using an iterative process. The positioning process starts with an initial guess, and improves the estimate at each iteration step by determining the local linear least squares solution.

If there are more observations than unknown variables, more accurate position estimation can be obtained by using the least squares method. In this case

x

is

calculated using the least squares approach as shown in Equation (5.29).

x

(H

T

H)

1

H

T

Δr

(5.29)

5.2.2 Comparing Taylor Series Method to Other Methods

In addition to Taylor series method, there are several methods proposed to solve the set

of hyperbolic Equations (5.19) (Aatique, 1997, p. 27).

Fang’s method

Friedlander’s method

Spherical-intersection method

Spherical-interpolation method

Divide-and-Conquer method

Chan’s method

These methods are discussed here briefly, but they are not presented in more detail.

Fang’s method provides an exact solution, when the number of TDOA measurements equals to the number of unknowns. However, Fang’s method (Fang, 1990) cannot make use of redundant measurements from extra base stations, which would improve

60 the positioning accuracy and reliability (Chan & Ho, 1994). This defect makes the use of Fang’s method impractical in most of the positioning systems. Friedlander’s method, spherical-intersection method and spherical-interpolation method utilize the redundant measurements, but their estimators are not optimum (Chan & Ho, 1994).

The divide-and-conquer method can achieve the optimum performance when the errors are small, but the Fischer information matrix is large (Aatique, 1997, p. 27).

Chan’s method is the preferred method in many papers (Chan & Ho, 1994; Dickerson

& al., 2005; Shin & Sung, 2002). Chan’s method is a closed-form solution (noniterative) and it can make use of redundant measurements. With low noise data, the performance of the Chan’s method is excellent, but when the noise increases, it has been reported to have some difficulties to provide the location (Li & al. 2005;

Mizusawa, 1996).

The Taylor series method has been proven reliable and accurate, and it works well even in the presence of noise and NLOS error (Li & al., 2005; Mizusawa, 1996).

However, Chan & Ho (1994) criticize the Taylor series method to be converging towards a local minimum, if the initial guess is not close enough to the true position.

Chan & Ho also state that Taylor series method may be computationally intensive.

However, in GPS calculation the Taylor series method goes to the local minima very seldom. Even in the case when the initial guess is in the centre of the earth, the Taylor series method converges towards the true position with four or five iterations only. If the solution goes to a local minimum, a new initial guess can be selected randomly, and the iteration can be tried again. In case of local positioning systems, the initial guess can be set to the middle of the search area surrounded by the base stations. In this case it is not probable that the Taylor series method would provide an incorrect local minimum as a result.

According to Aatique (1997, p 27) the linearization process can introduce significant errors when DOP value is bad. With a bad DOP value relative small ranging error can result a large positioning error. It has been shown that eliminating the second order terms of Taylor series expansion can provide significant errors in this situation.

However, in practical local (or global) positioning systems the computation should only be done with relative good DOP values. The inaccuracy due to eliminating the higher order terms of Taylor series expansion is very small when compared to the typical ranging accuracy.

Taylor series method can be easily improved so that the erroneous measurements due to NLOS conditions are detected and identified. In case of over-determined solution,

61 the erroneous observations can be identified and removed by using Receiver

Autonomous Integrity Monitoring (RAIM) algorithm (Kaplan, 1996, p. 306).

5.3 Angle of Arrival Techniques

In AOA technique the position of the mobile terminal is calculated from the angle of arrival measurements of the received signals. In two dimensional systems the azimuth angle

 from the base station (BS) to the mobile terminal (MT) is measured. In three dimensional systems also the elevation angle

is measured. For simplicity, this chapter presents only the two-dimensional computation. The three-dimensional computation is explained in (Du & Lee, 2004).

The geometry of the AOA location technique is shown in Figure 14.

Figure 14. AOA Positioning technique

The measured angle of arrival

i

from base station i and the measurement error

n i

satisfy (Vidal & al., 2001, p. 25),

i

i

0

n i

(5.30) with

i

0

 arctan



y u x u

y x i i

 (5.31)

62 where (x

u

, y u

) are the mobile station coordinates and (x

i

, y i

) are the coordinates of the

ith base station. Equation 2.4 can be rewritten as (Du & Lee, 2004)

n i

i

i

0

(5.32)

Taking the sine of both sides of the equation 2.7 and multiplying by r we have (Du &

i

Lee, 2004)

r i

sin

n i

r i

sin(

i

i

0

)

(5.33) or

r i

sin

n i

r i

sin

i

cos

i

0

r i

cos

i

sin

i

0

(5.34) where

r i

 base station.

(

x u

x i

)

2

(

y u

y i

)

2

is the distance from the mobile terminal to the ith

Figure 15. The geometry of AOA location method

From Figure 15 we obtain (Du & Lee, 2004)

x u

x i

r i

cos

i

0

y u

y i

r i

sin

i

0

Combining the equations (5.34) and (5.35) we get (Du & Lee, 2004)

(5.35)

63

r i

sin

n i

(

x u

x i

) sin

i

(

y u

y i

) cos

i

(5.36)

To simplify the equation, it is assumed that the measurement error of the angle is very small, i.e., |n

| << 1 and therefore sin

n

i n i

(Du & Lee, 2004). Based on this assumption, equation 2.10 can now be written in linear form

0

 

(

x u

x i

) sin

i

(

y u

y i

) cos

i

r i n i

(5.37)

Arranging the unknown variables to the left hand side of the equation we obtain

x u

sin

i

y u

cos

i

x i

sin

i

y i

cos

i

r i n i

(5.38)

Equation (5.38) is linear with respect to x and y. The position of the target object (x, y) can be solved now, if there are two equations of form (5.38). The equation 2.14 can be

expressed in matrix form as

h

Fx

(5.39) where

F

 sin sin

1

2 cos

1 cos

2

x

x y

h

x x

2

1 sin

1 sin

2

y

1

y

2 cos

1 cos

2

(5.40)

Equation (5.39) has a solution

x

F

1

h

The over-determined solution is

x

(

F

T

F

)

1

F

T h

(5.41)

(5.42)

5.4 Location Fingerprinting

Location fingerprinting is a position estimation algorithm based on pattern-matching.

Usually the systems employing the location fingerprinting method use RSS measurements to obtain the location of the target device. However, the RSS

64 measurements are not used to estimate the distances between the base stations and the target device, neither trilateration is employed for position estimation. Instead, the position of the target device is obtained by finding the best match between a vector of measured RSS values and a vector of known ―fingerprint‖ stored earlier in database.

The fingerprinting technique is relative simple to deploy, when compared to AOA or

TOA/TDOA range estimation techniques. No specialized hardware is needed at the mobile device or base stations, and any existing wireless LAN infrastructure can be reused for the positioning system. Currently, the location fingerprinting is one of the most common methods used in indoor positioning systems. Example of a commercial product utilizing the location fingerprinting algorithm is Ekahau (Ekahau, [Cited

January 24, 2008]).

The location fingerprinting technique fundamentally assumes that each potential location in the target area ideally possesses a distinct unique RF ―signature‖. The closer the reality is to this ideal, the better the performance of the location fingerprinting solution (CISCO Systems Inc., 2006).

In principle, the location fingerprinting technique can be used both with the selfpositioning systems and network centric systems. In Ekahau’s system, the RSS is estimated at the mobile terminal. The measured RSS is then sent to the Ekahau positioning engine running on the server. Ekahau’s system is thus a network centric approach. In this text it is assumed that the RSS values of the signals transmitted by the base stations are estimated at the mobile terminal.

The location fingerprinting process contains two phases: the offline phase and the online phase. In the offline phase the RSS values are measured from multiple access points at each point in the positioning area. This vector of RSS measurements, called location fingerprint, is stored to the database along with the position coordinates of that point. In the on-line phase the mobile device will measure the RSS from multiple access points. The vector of measured RSS samples is then compared to the location fingerprints stored in the database. The position associated with the location fingerprint having the closest match to the measured RSS vector is selected as the position estimate.

There are two methods to build the database. In the first method based on the empirical measurements the user of the mobile device walks around in the building. At different locations the user records the physical coordinates and the signal strengths from each

65 access point in the range. In the second method the database is constructed using a mathematical model of indoor radio propagation (Bahl & Padmanabhan, 2000b).

5.4.1 Offline Phase

In the offline (calibration) phase the entire area is first divided to a rectangular grid of points. The location fingerprints are then collected by performing a walk-around of the target environment with a mobile device. At each point of the grid, the mobile terminal measures the received signal strength (RSS) from multiple access points. This vector of RSS values is called location fingerprint, and the size of the vector for this sample point equals to the number of base stations that can be detected by the mobile device.

The vector of RSS values at that point is stored to the database with the position information of that point. Two location fingerprints and their respective locations are

shown in Figure 16.

Figure 16. Example of location fingerprints estimated for two locations using four base stations (BS) (CISCO Systems Inc., 2006)

During the offline phase the RSS values are measured with enough statistics before the location fingerprint is stored to the database.

66

5.4.2 Online Phase

In the on-line phase the mobile device will measure the RSS from multiple access points. The vector of measured RSS samples is sent to the central server for position computation. Another technique is that a group of access points collect the RSS measurements from a mobile device and send them to the central server. The server estimates the location of the mobile device and reports the estimate back to the mobile device or to another application requesting the positioning information.

A simple approach to estimate the location of the mobile device is to compare the measured RSS vector to each fingerprint in the database. The location of the mobile device is the point in the grid having the fingerprint closest to the measured sample vector. The most common algorithm to find the best match between the measurement and the fingerprint is to compute the Euclidean distance between these vectors. In location fingerprinting the Euclidian distance is also called signal distance

(Kaemarungsi & Krishnamurthy, 2004). Two vectors are normally used in the location fingerprinting technique to estimate the position of the mobile device. The first vector called sample RSS vector consists of sample RSS measured at the mobile device from

N access points. Sample RSS vector is denoted as:

R

1

,

2

,

3

,...,

N

. The second vector called location fingerprint vector consists of the true means of all the received signal strength random variables at a particular location from N access points. The values are measured at the off-line phase and recorded in the location database. This vector is denoted as:

~

R

r

1

,

r

2

,

r

3

,...,

r

N

.

The signal distance between the sample RSS vector and the average RSS vectors is calculated as follows (Kaemarungsi & Krsihnamurthy, 2004):

Z

N

i

1

(

i

r i

)

2

1

2

i

N

1

(

q i

)

2

1

2

(5.43)

The location fingerprint vector that minimizes the distance in signal space correspond the physical coordinates as its estimate of the user’s location. This simple method is called nearest neighbor in signal space (NNSS) (Bahl & Padmanabhan 2000a).

Several improvements have been developed for the NNSS method. There are also other algorithms which use neural networks (Small & al., 2000) or Bayesian modeling

(Castro & al., 2001) to relate the sample RSS vector to the fingerprint in the database.

Ekahau system is based on Bayesian modeling approach (Eissfeller & al., 2004).

67

5.4.3 Improvements to the NNSS Method

In practice, the actual location is not exactly in the same place as the locations stored in the radio map. As a consequence, there can be several location fingerprints whose signal distance is very close to the fingerprint having the minimum signal distance.

There is no reason to pick just the closest one and discard others that are almost as close. The NNSS algorithm can be modified so that a small number of closely matching fingerprints is used instead of using only the closest one. The estimate of the user’s position is obtained by averaging the physical coordinates of these fingerprints.

Bahl and Padmanabhan (2000b) call this method as NNSS-AVG. They found that for small number of nearest neighbors there is a small improvement over the single nearest neighbor approach. For large number (more than 8) of nearest neighbors the location estimation error is increased.

Another method to improve the NNSS method is to take the user’s dynamics into account. Bahl and Padmanabhan (2000b) have developed a method which they call continuous user tracking. The idea behind the continuous user tracking is to utilize the position information from the past. The user’s position at a given time instant is likely to be near that at the previous time instant. In this method the nearest k NNSS values are stored to a buffer of depth h. A Viterbi-like algorithm searches a path between

NNSS values which minimizes the path between the oldest and newest value in the buffer. Another possibility to utilize the continuous user data is to use an Extended

Kalman Filter to smooth the position data. Ekahau system utilizes also the digital map of the building so that impossible paths of the mobile terminal (those going through walls) are rejected (Eissfeller & al, 2004).

5.4.4 Effect of the Environment and Infrastructure on Performance

Various parameters of the environment and the infrastructure of the system affect to the performance of the location-based system. The performance of location fingerprinting algorithm depends on the user’s orientation, number of base stations, grid spacing used, and number of samples collected during the offline and online phase. These factors are studied in the following sections.

Impact of the User Orientation

Bahl & Padmanabhan (2000a) discovered that signal strength at a given location varies significantly depending on the direction the user is facing. In one orientation, the antenna of the mobile device may have LOS connectivity to the antenna of the access

68 point, while in the opposite orientation the user’s body may form an obstruction.

Because human body consists of 70% water and the resonance frequency of the water is 2.4 GHz, the signal is absorbed when the user obstructs the signal path (Ladd & al.,

2002). Kaemarungsi & Krishnamurthy (2004) found that received signal strength may attenuate more than 9 dBm due to the obstruction of human body. The attenuation of the body may completely block the signal from an access point. Similar results were obtained by Bahl & Padmanabhan (2000a). These results suggest that user’s orientation is crucial and should be taken into account in the computation.

Bahl and Padmanabhan (2000a) developed the location fingerprinting method so that the user’s orientation is also recorded during the off-line phase. For each physical location the received signal strength is measured in east, north, south and west directions of the building. All of these four signal strength measurements are stored in the database separately, so that there are four individual signal strength values for each physical location. During the online phase the measured signal strength is compared to the fingerprints of each location and cardinal direction. This approach will need four times as much data to be stored in the database as the original approach, but would improve the accuracy of the positioning.

Effect of the Number of Access Points

Intuitively, increasing number of access points improves the accuracy and precision of the location estimated by the location fingerprinting technique. This holds also to the navigation systems using TOA or AOA measurements. Increasing the number of base stations improves the geometry. Additionally, the extra observations make the effect of noisy or incorrect observations smaller.

In location fingerprinting increasing the number of access points improves the probability of returning the correct location. According to the tests a higher number of access points improves the precision, but the probability does not increase significantly if the number of access points is bigger than five (Kaemarungsi 2005; Bahl &

Padmanabhan 2000b). If the RSS measurements have large standard deviation, the high number of access points has more effect on the accuracy and the precision.

Kaemarungsi (2005, p. 138) recommends that at each data point the receiver is able to track at least four access points.

In principle, all the points in the area may have unique location fingerprints even if there is only one access point. However, probability that two or more points share a fingerprint with values close to each other is high. In practice, at least three access

69 points are needed in most of cases. The mutual locations of the access points also affect to the performance. If the access points are far away in the same direction from the research area, the fingerprints of the points in this area are probably close to each other.

Impact of the Grid Spacing

In location fingerprinting method the research area is covered by a rectangular grid of points. The distance between two adjacent physical locations is called grid spacing and reported in meters or feet (Kaemarungsi, 2005, p. 6). The grid spacing is selected prior to the site-survey. Large grid spacing provides poor accuracy or granularity.

Kaemarungsi (2005, p. 110) examined the probability of returning the correct location when the grid spacing goes smaller (the area of the point reduces). He found an obvious result: the probability of returning the correct location reduces with small grid spacing. Bahl & Padmanabhan (2000a) measured the error distance (accuracy) between the estimated and true locations while increasing the number of data points.

They found that increasing the data points first diminished the error distance rapidly, but with large number of data points the error distance did not diminish any more.

Kaemarungsi (2005, p. 139) suggests that grid spacing should be greater than 1.25 m.

Impact of the Number of Samples

Collecting enough statistics during the off-line phase is essential to achieve good performance. If the positioning system only uses the mean values of RSS to create fingerprints, a small number of samples is sufficient. In RADAR system Bahl &

Padmanabhan (2000a) found that order of 20 samples is sufficient. Even with a smaller number of real-time samples the accuracy starts to approach the accuracy obtained using all of the samples. They found that with 1 real-time sample, the error distance was 30% worse than with all of the samples. With 2 samples, it was 11% worse and with 3 samples it was under 4% worse. Larger number of samples (order of 300) is needed for the probabilistic approach to create accurate diagrams (Kaemarungsi, 2005, p. 72). Also the sampling period of the device affects how many samples are needed.

According to the results of Kaemarungsi (2005, p. 73) only 30 to 50 samples would be sufficient for a location fingerprint on the mean values only. If the distribution of the

RSS values is needed, the distribution can be completely described by the mean and variance. Therefore, the convergence of standard deviation can be used as a condition to stop collecting new samples. According to the results of Kaemarungsi research, a number between 150 and 200 should be sufficient.

70

Kaemarungsi (2005, p. 123) indicated that during the online phase he used only one sample to detect the location.

5.4.5 Radio Propagation Model

Instead of building the radio map empirically, i.e. measuring the RSS manually from multiple access points at each point in the grid, the radio map can be built by modeling the radio wave propagation. Bahl & Padmanabhan (2000b) have studied using the radio propagation model in location fingerprinting. The purpose of using radio propagation model was to decrease the amount of time needed to take measurements in the building. At the offline phase the theoretically computed signal strengths are calculated for each grid point on the radio map. At the online phase the NNSS algorithm or its variants are used to determine the user’s location by the same way as with the empirically constructed radio map. Obviously the performance of this method is directly impacted by the quality of the propagation model.

At indoor environment the signal propagation is dominated by reflections, diffraction and scattering of radio waves caused by the walls and other obstacles within the building. Generally the transmitted signal reaches the receiver via multiple paths.

Signal components arriving from direct and indirect paths combine to produce a distorted version of the transmitted signal. This affects also the received signal strength. Because of the multipath, modeling the radio wave propagation indoors is very challenging. Bahl & Padmanabhan (2000b) examined three different models and chose the Wall Attenuation Factor (WAF) propagation model for further examination.

In this method the theoretical signal strengths were estimated as a function of distance and the number of walls in the path from the access points to the grid points. A few measurements are beneficial to determine the actual attenuation caused by each wall.

The reported results of the radio propagation model were worse than the results of the empirical method.

5.4.6 Aliasing

One problem in location fingerprinting is a phenomenon which Bahl & Padmanabhan

(2000b) call aliasing. Large error in position may occur when two points are far apart physically but very close together in signal space. Such aliasing may happen indoors due to complex radio signal propagation environment. A point close to an access point may have similar signal strength as another point far away. This may happen when the signal strength measured close to the access point is attenuated by an obstruction and

71 the far apart point has an unobstructed signal. How often the aliasing can occur, depends on the building layout and the number of access points and their location.

72

73

6 Indoor GPS

This chapter presents an overview of GPS-based indoor positioning technologies and compares these technologies to the other indoor location techniques and to the local positioning systems.

6.1 Introduction

Positioning technologies intended specially for indoor use have many advantages over the Global Positioning System. For example, systems based on WLAN and UWB provide superior indoor positioning performance when compared to the satellite-based navigation systems. However, GPS is still an attractive method for the indoor positioning in some applications, where the positioning accuracy is not the most important requirement. Today GPS is the most commonly used navigation system, which makes it a potential alternative also for indoor use.

Navigation systems based on a local radio network have the drawback that they can only be used locally, on the coverage of the local antennas or beacons. Most of the local positioning systems are also network-centric: the position of the moving targets must be calculated in the server from the measurements made by the fixed receivers. In centralized systems the moving object cannot measure the distances to the beacons and calculate the position by itself. If the objects to be located are persons, the centralized architecture may hurt the individuals’ privacy.

The need for the GPS indoor location ability was first found in the mid-1990s when

U.S. FCC decided to require ability to locate the mobile phone in case of emergency call (E911). Many users require continuous outdoor and indoor availability, and using a GPS receiver indoors has some advantages. The GPS module receives the satellite signals and computes the position of the object, and no one else needs to know the user’s location. The same GPS receiver can be used everywhere if the coverage of the

GPS is extended to the environments where the signal is attenuated.

The GPS signal received at the Earth’s surface is very weak. According to IS-GPS-200

(2004) specification, the power level of the GPS coarse acquisition (C/A) code on the earth’s surface is about -158.5 dBW (i.e. -128.5 dBm). At indoor use the structures of

74 the building attenuate the weak GPS signal further. According to Dedes and Dempster

(2005) the GPS receiver needs to be able to track signals with power levels ranging from -160 dBW to -200dBW to operate indoors. Standard GPS receivers designed during 1990’s are not able to track such weak signals. However, due to the recent development in GPS technology, most of the modern GPS receivers are able to track the GPS satellites also indoor buildings, but with lower accuracy.

In the following sections three methods are discussed to help the GPS receiver operate better in weak signal conditions. First, the GPS receiver sensitivity can be improved by integrating the GPS signal for a longer time and by assisting the receiver with external aiding information. Second, pseudolites transmitting a GPS-like signal can be installed inside building. Third, GPS signal received at a roof antenna can be radiated inside building.

6.2 Assisted GPS and High Sensitivity GPS

Most of the GPS receivers have separate acquisition and tracking modes. GPS signal acquisition has traditionally been a slow process. The acquisition process can be speeded up by using external aiding information, such as satellite orbit parameters,

GPS time and approximate position. This concept of using the aiding information is called assisted GPS. According to Diggelen & Abraham (2001) assisted GPS provide

10 dB increase in sensitivity, which is not enough for indoor operation. One would need at least 30 dB improvement over the traditional GPS receivers. In addition to the

GPS aiding, a technology called high sensitivity GPS (HSGPS) is needed to enable

GPS operation indoors. In HSGPS technology the GPS signal is integrated for a long period, which improves the signal to noise ratio.

6.2.1 GPS Signal Acquisition

GPS signal acquisition is a time-consuming process for traditional GPS receivers.

Usually GPS receivers search the incoming signal in two dimensions: code phase and

Doppler frequency. In the code phase dimension the GPS receiver acquires the satellite signal as follows: The GPS receiver generates a replica of the PRN code that is transmitted by the satellite. The phase of the replica code is shifted until it correlates with the satellite PRN code. When the phase of the receiver generated replica code matches the phase of the incoming satellite code, there is maximum correlation. When the phase of the replica code is offset by more than one chip of the incoming satellite signal, there is minimum correlation. The GPS receiver must also detect the satellite in carrier phase dimension by replicating the carrier frequency and Doppler. The Doppler

75 is due to the relative dynamics between the satellite and the receiver. Also the receiver’s reference oscillator instability affects the frequency uncertainty. Thus, the

GPS signal acquisition and tracking is a two dimensional (code and carrier) process

(Kaplan, 1996, p. 119). This process is illustrated in Figure 17.

Figure 17. Two-dimensional acquisition search space

The two-dimensional search space could have a Doppler of ± 4.5 kHz and a 0-1022 chip C/A code phase. Correlation is performed in each cell (called bin) by using predetection integration and comparing the correlation value with the detection threshold. The search goes on to the next bin until the correlation value is more than the threshold. Usually two samples per chip are used in searching in the code phase.

Because there are two channels per satellite (In-phase and Quadrature-phase), there is a total of 4092 samples. The width of the bin f

c

in the frequency dimension is obtained from the equation (Singh, 2006b, p. 19)

f c

3

2

N

where N is the signal integration time.

(6.1)

For strong GPS signals, an integration time of C/A code repetition time (1 ms) is sufficient (Dedes & Dempster 2005). This results 667 Hz frequency width. In this case the total number of frequency bins would be 4092 code samples * 9.0 kHz / 667 Hz =

76

55214 bins. Increasing the integration period increases the number of search bin needed. Long integration time is used in HSGPS receivers when the signal weak. For

20 ms integration time the frequency width is 33 Hz and the number of search bins is

549672.

6.2.2 Assisted GPS

Typically GPS receivers have been designed to dwell for about one millisecond in each search bin (Diggelen & Abraham, 2001). If the number of bins is 50000, then it would take 50 seconds to search the entire frequency/code delay space. The need to make the acquisition process faster has led to an idea of assisted GPS. The search area can be reduced significantly if the visible satellites, their Dopplers and relative code delay offsets are known in advance. In assisted GPS the initial position estimate, time estimate and satellite ephemeris can be provided from the external source. The receiver can then estimate the satellite Dopplers and relative code delay offsets. As a result the frequency/code delay space to be searched is reduced significantly.

According to Diggelen & Abraham (2001) the range of possible frequencies can be reduced by a factor of ten by using GPS aiding information. As a result the acquisition time is reduced to 10 percent of the original value. It is interesting that instead of speeding acquisition, one could make use of the aiding to increase the dwell time in each bin, thereby increasing the sensitivity. However, Diggelen & Abraham calculate that this does not improve sensitivity enough to allow indoor operation. If the search space is reduced by a factor of 10, then the dwell time in each bin could be increased from 1 to 10 ms, without increasing the total search time. According to Diggelen &

Abraham increasing the integration time from 1 ms to 10 ms yields a signal to noise ratio gain that approaches 10 dB. However, this is not enough since GPS signal levels indoors are from 20 to 30 dB below the signal levels outdoors. As a result, aiding information provides significant improvement to the signal acquisition or receiver sensitivity, but it is not alone sufficient method to make a receiver work indoors.

Changes in the hardware architecture of the GPS receiver are also needed.

6.2.3 Weak GPS Signal Processing

When the signal is attenuated, integration can be done over a longer interval in order to increase the processing gain. Coherent integration interval can be up to 20 ms when

GPS signal navigation message bit transition occurs. If integration is performed over a longer period, the signal must be squared. Squaring also squares the noise, thus diminishing the net gain of non-coherent integration. Lachapelle (2004) claims that

77 coherent integration is often the only method used in the tracking mode. In signal acquisition, non-coherent integration of a few hundred milliseconds is often used.

However, Signav has demonstrated acquisition down to 183 dBW and tracking down to -185 dBW using 256 ms integration periods without external assistance (Bryant & al

2001).

When the integration period is long, the Doppler error may degrade the tracking performance. The receiver clock drift and the user antenna motion can cause significant phase changes and effectively limit the integration time. The signal integration in conventional receivers is not a real time process. In other words, there is no feedback from the GPS signals to the local oscillator during the integration period.

Lachapelle (2004) proposes that using a higher grade clock reduces the effect of the clock drift component. He also suggests that the user motion component may be eliminated by using inertial measurement unit. However, both the high grade clock and the inertial measurement unit are too expensive components for a cell phone and other portable devices.

Diggelen & Abraham (2001) present a hardware approach which enables long integration periods without the need of high precision clock or inertial measurement unit. This approach contains a convolution processor which performs a real-time convolution of GPS signals over the entire range of possible code delays. The convolution processor contains over 2000 correlators per satellite, and it can compute all possible correlation delays in real-time. According to Diggelen and Abraham, this design makes the separate acquisition and tracking modes obsolete. Because the hardware processing approach is a real-time implementation, it supports GPS-based frequency adjustment. The feedback loop from the convolution processor drives the local oscillator to produce the correct frequency.

Assisted GPS is not used in all cellular network techniques. Also concrete walls of the buildings block the GPS signal completely. Supersensitive assisted GPS may be too expensive and it is still not able to navigate in all places.

6.3 Pseudolites

Pseudolites are ground-based GPS signal transmitters which can improve the GPS signal availability. Pseudolites can be installed inside buildings or other places where the GPS satellite signal coverage is poor or completely missing. Pseudolites use low cost temperature controlled oscillators while the GPS satellites use high precision atomic clocks. Usually the local navigation system consists of at least four pseudolites.

78

Sometimes the signals from the GPS satellites can be used together with the pseudolites. Usually the clocks of the pseudolites are synchronized to each other and also the GPS (satellite) time.

The pseudolites are still too expensive for many purposes, but there are plans to use the pseudolites to help airplane landing system, which requires CAT-III. Another application is to augment the GPS navigation in deep open mines, where only the highest elevation GPS satellites are visible.

A problem with pseudolites is so called near-far problem. The received signal strengths of the satellites and the pseudolites have to be on the same level. All the satellites are within the distance of 20000 - 26000 km from the user. The received signal level is always on the same range. However, the pseudolites are quite close to the user, probably within tens of meters, and the relative distance to the pseudolites varies quickly when the user moves. When the user is near the pseudolite, the receiver cannot track the other pseudolites or GPS satellites. Some pseudolite systems use time switching, which enables only one pseudolite to transmit the signal at time. Usually the standard GPS receiver hardware works with pseudolites, but the GPS receiver firmware requires some modifications due to the different PRN codes and navigation message.

The L1 band is protected globally by legislation. In general, it is illegal to transmit on

L1 frequency band without an approved radio licence.

6.4 GPS Repeater

GPS repeater consists of a GPS antenna located at the roof of the building, a GPS reference receiver and an amplifier that amplifies the signal to be radiated inside the building. The GPS signal can then be tracked inside the building with a GPS receiver.

It is well known that in this case the GPS receiver measures the roof antenna position.

However, the fourth unknown component of the navigation solution, the clock bias, changes when the distance from the user to the radiator changes. This information can be used to solve the position of the indoor receiver. Four repeaters are needed to calculate the user position. Because the receiver can track signals only from one repeater at time, the repeaters must be synchronized so that only one repeater transmits at time. Standard GPS receiver cannot be used without modifications to the firmware

(Bu & al., 2003).

79

7 Local Positioning Systems

This chapter presents examples of local positioning systems based on different signal technologies and computation methods. The local positioning systems discussed in this chapter utilize ultrasound pulses, and WLAN and UWB radio signals.

7.1 Ultrasound-Based Positioning Systems

Ultrasound technology provides great ranging accuracy. Because the speed of sound is relative low (340 m/s), the accuracy requirement for measuring the propagation time is also low. For example, only 3 ms timing accuracy is needed to measure the distance within one meter accuracy. In contrast, using RF signal propagating at the speed of light, 3 ns timing accuracy would be needed.

The TOA method is most often used in ultrasonic positioning. In TOA positioning the transmit time of the ultrasonic pulse is needed. In many ultrasonic systems, the originator device transmits a RF pulse and an ultrasonic at the same time. The recipient device measures the difference between the arrived RF and ultrasonic pulses, which gives an estimate for the ultrasonic pulse propagation time. The target object is located at a circle whose center is at the base station and whose radius is the product of the measured propagation time and the speed of sound. The position of the object can be solved by the same way as in GPS. Only three measurements are needed for threedimensional positioning.

In spite of high accuracy of the ultrasound technology, it is not the most common indoor positioning technique. Ultrasound sensors react to ambient ultrasonic pulse and high-energy sound pulses, which makes ultrasound positioning technique vulnerable in many indoor environments.

The most well known positioning systems in research community are MIT Cricket

(Priyantha, 2005) and Active Bat (Ward & al., 1997). Both systems obtain the propagation time of the ultrasonic pulse by measuring the time difference between incoming RF and ultrasonic pulse. The MIT Cricket system is a user centric system.

The fixed base stations work as transmitters and the mobile node receives the signal.

The Active Bat system is network centric. The mobile node transmits the RF and

80 ultrasonic pulses and the base stations receive the signals. The position of the mobile node is calculated at the network. Another example of a network-based ultrasound positioning system is Sonitor ([Cited January 24, 2008]), which is primarily intended for tracking patients and objects in hospitals. MIT Cricket system and Active Bat systems are discussed in the following sections.

7.1.1 MIT Cricket System

MIT Cricket is an indoor positioning system based on ultrasound technology. MIT

Cricket provides two kinds of positioning information: Cartesian position coordinates and symbolic coordinates called space identifiers. Space identifiers are application specific names associated with spaces such as rooms (Cricket, 2008).

The Cricket system consists of the set of base stations, called beacons, and mobile devices called listeners whose location needs to be obtained. The beacons are deployed on walls or ceilings. A listener is attached to a host device, like handheld or laptop computer. At the system setup, the administrator configures the position coordinates and the space identifiers of the beacons. Each beacon periodically broadcasts its space identifier and position coordinates on a RF channel. Each beacon also broadcasts an ultrasonic pulse at the same time as the RF message.

To infer the distance from the beacon, the listener measures the time difference between arrived RF and ultrasonic pulses. The listener sends the measured distance and the beacon position information obtained from the RF message to the host computer. The host computer calculates the listener position coordinates based on distances from multiple beacons. The computation algorithm associates also the listener with the space corresponding to the nearest beacon. The spaces may be separated by physical boundaries like walls, or they may be virtual. Because ultrasound does not travel through walls, Cricket can easily demarcate spaces separated by walls.

Because the position is calculated at the target device, the system administrator cannot track the users. This is a good issue if privacy is a desired goal. In network centric ultrasonic system the location is calculated at the network base station, and ability to track users is thus inherent.

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7.1.2 Active Bat System

Like MIT Cricket, the Active Bat system developed by AT&T Laboratories at

Cambridge is intended for indoor positioning. Unlike MIT Cricket, Active Bat is network centric, which means that the objects to be located, called bats, are active. The bat transmits ultrasound pulse, which is received by the receivers located at fixed coordinates. Like MIT Cricket the Active Bat system uses radio signal for transmit time synchronization.

The Active Bat system consists of a bat which is attached to an object to be located and a set of receivers deployed to the roof of a room. The system also has a base station which sends a synchronization signal to the receivers and the bats. The base station is responsible of calculating the position coordinates of the bats (Ward & al.,

1997).

The base station sends simultaneously a timing signal to the fixed ultrasound receivers and a radio signal to the bat. When the bat receives the radio signal it transmits ultrasound pulse, which is received by multiple ultrasound receivers at the roof. The ultrasound receivers measure the time difference between the timing signal and the ultrasonic pulse and send times of flight of the ultrasound pulses to the base station.

The base station computes the distances between the bat and the receivers and the bat position coordinates using TOA algorithm (Ward & al., 1997).

7.2 WLAN-Based Positioning Systems

It is often beneficial to utilize the existing communications infrastructure for indoor positioning. Many offices, hotels and airports have a wireless LAN system installed to serve the communication needs of people. In addition to wireless communication,

IEEE 802.11b or g WLAN systems can be used for indoor navigation. Most new laptop computers and many smart phones are equipped with WLAN device to provide access to the wireless network. Positioning infrastructure can be built for example to an office building by using only the existing WLAN infrastructure and the user devices without adding any new hardware to the system. The positioning function is added to the communication system by adding server software responsible of position estimation and client software to be installed to the mobile terminal. Low-cost WLAN tags having the client software can also be attached to people and assets.

Positioning systems utilizing standard WLAN access points and other components use received signal strength (RSS) as the positioning observable. Usually, the RSS

82 measurements are not used directly to form range estimates. Instead, the position of the user is estimated by location fingerprinting, where the observed RSS values are compared to the table of previously measured RSS values. Examples of WLAN-based positioning systems utilizing the location fingerprinting are Microsoft RADAR (Bahl

& Padmanabhan 2000a, 2000b) and Ekahau (2008).

Also the time difference of arrival (TDOA) can be measured from WLAN signal. If the TDOA observation is obtained, the position of the object is calculated using hyperbolic positioning (trilateration). TDOA cannot be measured with standard

WLAN access points, and additional hardware and signal processing is needed to synchronize the base stations to common time and to estimate the time difference of arrival.

Cell of origin technique is not suitable for indoor positioning with WLAN because cells are too wide. Also, the WLAN standard devices keep connections to access point even if a stronger signal from other access point is available (Rerrer & Kao, 2005).

7.2.1 Aeroscout Positioning System

An example of WLAN-based positioning system using TDOA observables and hyperbolic positioning is Aeroscout (2008, [Cited January 24, 2008]). Aeroscout system uses WLAN to locate any standard 802.11b and g device, such as cellular phones, PDAs and laptop computers. In addition, the Aeroscout system locates battery powered Aeroscout tags attached to people or assets. Seinäjoki University of Applied

Sciences has used Aeroscout positioning system to track cattle inside a building.

The Aeroscout system is able to locate the object using either TDOA or RSS measurements. If RSS observable is used, the Aeroscout system is able to utilize standard WLAN access points as positioning receivers. In this case the existing

WLAN installation can be utilized. However, only the Aeroscout’s location receivers are able to provide TDOA measurements. In addition, the location receivers have to be synchronized accurately to common time. TDOA is used for large open indoor environments and outdoors while RSS may perform better in a tight indoor environment. RSS positioning is based on trilateration, not on location fingerprinting.

Aeroscout System Architecture

The architecture of Aeroscout positioning system is shown in Figure 18 (Aeroscout,

[Cited January 24, 2008]).

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Figure 18. Aeroscout system components (Aeroscout, [Cited January 24, 2008])

Aeroscout system’s hardware components are:

IEEE 802.11 wireless local area network

Standard WLAN access points

Aeroscout location receivers

Server computer running the Aeroscout engine

Client WLAN devices (standard WLAN devices or Aeroscout tags)

Aeroscout’s software environment consists of the following components:

Aeroscout engine

Aeroscout mobile view

Third party location-based applications

The Aeroscout tags are active RFID tags using the WLAN standard. The tags enable the Aeroscout positioning system to locate people and assets otherwise not connected to a wireless network. Aeroscout T3 tag contains motion and temperature sensors and two call buttons. Unlike in the Ekahau system, no client software is needed in the standard WLAN devices.

The location receiver measures the arrival time of standard 802.11b and g messages and sends that information to the Aeroscout engine for further processing. Each

84 location receiver can process over 300 location measurements per second. Location receivers can also process presence-based location for areas where there are not enough receivers available for trilateration (Aeroscout, [Cited January 24, 2008]).

In Aeroscout system, like in all TDOA systems, the location receivers are synchronized to common time. In Seinäjoki research laboratory installation one of the location receivers acts as a master. The time difference between the master location receiver and ordinary location receiver is obtained by subtracting the measured distance from the theoretically calculated distance between the fixed receivers at known locations (Aljadeff & Granot, 2005).

Aeroscout Engine processes the information received from the location receivers and standard WLAN access points. To calculate the location of the user Aeroscout engine uses TDOA or RSSI method. Aeroscout engine contains positioning server software, which provides location or presence data to the MobileView software or third party applications. Third party applications can access the position data through XML API using TCP/IP sockets.

Aeroscout Exciters provide RFID detection capabilities, using the same tags that can also be used for positioning. The exciters provide RFID choke point functionality by triggering transmissions from the tags as they pass through doors, gates and other choke points (Kolodziej & Hjelm, 2006, p. 260).

7.2.2 Ekahau System

The Ekahau (2008) positioning system is based on IEEE 802.11 wireless LAN infrastructure. Ekahau system measures the received signal strengths of standard

WLAN signals and computes the location using the location fingerprinting technique.

Standard WLAN access points of any mark can be used and no proprietary infrastructure is needed. Ekahau system tracks Ekahau WLAN tags, laptops, PDAs, smart phones and other WLAN enabled devices (Ekahau, [Cited January 24, 2008]).

Examples of applications where Ekahau system has been used are patient and asset tracking in hospitals, industry and warehouses.

System Architecture

The system architecture of Ekahau Real-Time Location System (RTLS) is shown in

Figure 19 (Ekahau, [Cited January 24, 2008]).

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Figure 19. Ekahau system components (Ekahau, 2008)

Ekahau system’s hardware components are:

IEEE 802.11 wireless local area network

Server computer running the Ekahau positioning engine (EPE)

Client WLAN devices running the Ekahau client software

Ekahau’s software environment consists of the following components:

Ekahau Positiong Engine (EPE)

Ekahau Client

Ekahau Site Survey

Ekahau Client is a software component that runs on a client device (laptop, smart phone, PDA, WLAN tag). Ekahau Client reads the signal strength measurement data from the network adapter and sends that data to the positioning engine. Only tens of bytes data is transferred per location request (Kolodziej & Hjelm, 2006, p. 237).

Ekahau supports plenty of WLAN adapters. A problem is that different WLAN adapters measure the signal strengths differently and thus the measurements are not compatible. Ekahau tries to make different adapter measurements compatible with the measurements of the adapter used for building the model (Salaur, 2005, p. 35).

Ekahau tags obviously contain the functionality of the client software. There are three generations of the tags, named as T101, T201 and T301, and the tags can be attached to people or assets. According to Ekahau (2008), the tags have battery life of up to 5

86 years. The tags have a motion sensor that activates the tag only when the tracked object is moving. The tags can be managed remotely within the WLAN coverage area.

The remote management automates the configuration changes, battery level monitoring and tag software updates. The tag contains two buttons, which can be configured to send alarms or other messages to the applications (Ekahau, [Cited

January 24, 2008]).

Ekahau Positiong Engine (EPE) runs on the server computer. EPE stores the radio map, reads the signal strength data send by the Ekahau Clients (and tags) and calculates the clients’ location. User applications can be integrated to EPE via HTTP and XML-based Application Programming Interface (API) and Software Development

Toolkit (SDK). EPE contains also a web-based management and tracking console, which can be used for system configuration and remote configuration of the location tags. The radio map is created during the off-line phase with assistance of Ekahau

Location Survey tool or with Ekahau’s standalone Site Survey application, which is intended for network planning. (Ekahau, [Cited January 24, 2008].)

7.3 UWB-Based Positioning Systems

UWB is a radio technology which can be used for short-range and high speed communications by using a large portion of the radio bandwidth. UWB can be used to refer any radio technology having relative bandwidth larger than 20% or absolute

bandwidth more than 500 MHz as illustrated in Figure 20 (Reed & al., 2008).

Figure 20. FCC definition of UWB. Relative bandwidth (f

H

-f

L

)/f

C

> 20% or total bandwidth is greater than 500 MHz (Reed & al., 2008).

In UWB technology extremely short duration pulses (sub-nanosecond) are used instead of continuous waves to transmit information. The short pulse generates directly a very

87 wide bandwidth signal. The spread bandwidth of UWB waveform is generated directly without individual bit modulation. The conventional spread spectrum waveforms

(direct sequence DSSS or frequency hopping FHSS) typically have 100% duty cycle and peak and average power levels are equal. In UWB the waveform duty cycles are typically less than one percent and peak-to-average can be quite large (Reed & al.,

2008).

7.3.1 Advantages of UWB

Wide bandwidth of UWB offers advantages for communications, navigation and radar applications. In all of these applications the large bandwidth improves reliability as the signal contains different frequency components. This increases the probability that the signal will go through in the presence of obstacles or interfering signals. In navigation and radar applications a large absolute bandwidth provides higher ranging accuracy.

For communications both large relative and large absolute bandwidth decrease the power spectral density, thus reducing interference to existing narrowband systems

(Gezisi & al., 2005). Extremely wide bandwidth offers obviously high data rate, even though the duty cycle is low. One important advantage in indoor navigation and communication is that UWB is relative immune to multipath cancellation effects (Reed

& al., 2008). UWB technology offers also low power consumption with low cost.

UWB transceiver can be implemented nearly all-digital, with minimal RF electronics.

7.3.2 Applications

UWB radars have been developed since the late 1960’s (Barrett, 2000). UWB has been used for short-range radio communications only in the early 1990’s, but has received much interest after the FCC allowed the use of unlicensed communications. The first commercial systems are intended for short range personal area networks (PANs) having a high data rate (Gezici & al., 2005). Also the first indoor positioning systems based on UWB are introduced recently (Multispectral, [Cited January 24, 2008]).

UWB offers much higher data rate than Bluetooth or 802.11 for personal area networking (PAN). Data can be transferred very fast between cellular phones, PDAs, cameras, MP3 players and laptops. UWB communications can also be integrated into automotive in-car services and entertainment. Driving directions, music and videos can be downloaded to car’s navigation and entertainment systems from PDAs or laptops or road side UWB stations.

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UWB is suitable for sensor networks as well. Typically, in sensor networks there is no need for high data rate communication, but the positioning capability is important.

UWB signaling is suitable for sensor networks because it allows centimeter accuracy and does not suffer from multipath effect. Positioning capability offers new markets for sensor networks, like in area of logistics, security, manufacturing and health care.

Examples of companies developing UWB technology for positioning applications are

Aetherwire ([Cited January 24, 2008]), Multispectral ([Cited January 24, 2008]) and

Ubisense ([Cited January 24, 2008]). In addition to radar applications, communications and location finding the ultra-wideband technology can be used to through wall imaging and medical imaging applications (Reed & al., 2007).

7.3.3 UWB in Positioning

UWB technology provides unique advantages for local positioning systems and indoor navigation. The use of short pulse RF waveforms provides inherent precision for TOA or TDOA measurements. The time of arrival of the sub-nanosecond duration pulse can be accurately determined with a sensitive and high speed receiver. The very wide bandwidth of the UWB signal increases the probability of some frequency components penetrating through or going around obstacles. In other words, both high time resolution and penetration capability make UWB signals suitable for ranging purposes

(Sahinoglu & Gezisi, 2006).

Figure 21. Multipath effect of narrowband signal (left) and ultra-wideband signal.

A unique advantage of the UWB is its ranging performance at indoor environments, where the main sources of error are the multipath propagation and non-line-of-sight

(NLOS) propagation. At indoor environment multiple copies of transmitted signal with

89 various attenuation levels and time delay arrive at the receiver. In narrowband systems like GPS the reflecting components may distort the direct path signal and make accurate timing difficult. In UWB the direct path signal can be distinguished from the

reflections, making the pulse timing easier, as illustrated in Figure 21.

However, the first peak which indicates the shortest path and the best range estimate may not be the strongest peak. If the direct line-of-sight (LOS) is obstructed, a reflected signal may provide a stronger peak. Multipath mitigation techniques for

UWB are still needed and they are presented in (Lee & Scholtz, 2002).

In sensor networks and other applications where the signal is used also for positioning, the data rate is usually relatively low. From regulatory perspective, the UWB positioning systems having low data rates are allowed to have significantly higher peak powers than the high data rate systems designed mainly for communications (Fontana

& al., 2003). Thus, a UWB positioning system can operate over significantly longer distances, as compared to a system intended for high data rate communications. Also the very low duty cycle waveform needed in sensor network and positioning system reduces the energy consumption. The sensor network nodes can be equipped with very small batteries and still have years of operational lifetime.

7.3.4 Standards and Regulations

This section discusses the European and American standardization work for UWB.

UWB Regulations in the United States

The United States Federal Communications Commission (FCC) allowed the use of unlicensed UWB communications in April 2002 (FCC 02-48, 2002). The devices intended for communication must operate with their -10 dB bandwidth in the frequency band 3.1-10.6 GHz. According to FCC rules, ―The equipment must be designed to ensure that operation can only occur indoors or it consists of hand held devices that may be employed for such activities as peer-to-peer operation‖. (FCC 02-

48, 2002, p. 12)

FCC has adopted a very conservative out of band emission mask to address concerns of companies which make or market indoor electronic equipment. The limits on unwanted emissions are even more stringent for hand held (outdoor) devices (FCC 02-

48, 2002, p. 27). The emission masks of indoor and hand held devices is shown in

Figure 22 (Reed & al., 2007).

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In addition, FCC requires that the hand held devices transmit only in communication with an associated receiver. The transmitter must stop transmission within 10 seconds unless it receives an appropriate acknowledgement from the associated receiver, and the acknowledgement will continue during the transmission at 10 second intervals.

This rule is a restriction for some wireless sensor networks, because two way communications is required.

Figure 22. FCC Emission masks for indoor and hand held (outdoor) devices (Reed & al., 2007)

Other UWB applications, such as vehicular radars, through wall imaging systems and surveillance systems have partly or completely different regulations for frequency band end emission levels.

UWB Regulations in Europe

During writing this thesis (Autumn 2007) UWB can be used legally only in the United

States, with the exception of Singapore, which allows experimental use. During recent years Europe has made significant progress towards regulation of generic UWB applications. The current European UWB regulation is defined in European

Commission (EC) decision 2007/131/EC (EC, 2007) and CEPT decisions

ECC/DEC/(06)04 (2007) and ECC/DEC/(06)12 (2006). Before UWB equipment can

91 be marketed, the national regulations or legislation must be made in member states to reflect the EC decision.

US FCC regulation allows -41.3 dBm/MHz EIRP emissions in the frequency band 3.1-

10.6 GHz. European regulation allows -41.3 dBm/MHz EIRP emissions only in frequency band 6-8.5 GHz and with limitations in frequency bands 4.2-4.8 GHz and

3.4-4.8 GHz. The draft of the European spectrum mask is shown in Figure 23

(Europcom, 2007).

Figure 23. ECC draft spectrum mask. The blue solid line shows the emission mask according to general decision for devices under 10.6 GHz. The blue dashed line shows the allowed power level if LDC is use. The red solid line shows the allowed power level if mitigation technique is used (Europcom, 2007).

CEPT decision ECC/DEC/(06)12 (2006) allows -41.3 dBm/MHz emissions in the frequency band 3.4-4.8 GHz for devices without low duty cycle (LDC) before 31

December 2010. After that only UWB devices implementing LDC will be permitted to operate at the power level -41.3 dBm/MHz. Until 31 December 2010 UWB devices are permitted with maximum mean EIRP density of 41.3 dBm/MHz (EC, 2007). After that appropriate mitigation technique like ―Detect and Avoid‖ DAA must be used. The motivation to allow the use of 4.2-4.8 GHz band temporarily is to allow FCCcompliant devices to be used in Europe for few years, until new standards are adopted

(Europcom, 2007).

Use of equipment using UWB is not permitted if the device is at fixed outdoor location or it is connected to a fixed outdoor antenna. The use of UWB in vehicles is not

92 permitted either. According to EC decision (EC, 2007) the potential interference caused by such uses requires further study.

In addition to the regulations defined in 2007/131/EC (EC, 2007) standardization is needed. European Telecommunications Standards Institute (ETSI) is defining standards for positioning systems based on UWB. ETSI standard EN 302 500 is intended for indoor and EN 302 501 for outdoor location tracking. These standards are not published yet.

IEEE Standard 802.15.4

IEEE standard 802.15.4 (2007) defines the protocol and compatible interconnection for data communication devices using low data rate radio frequency transmissions in a wireless personal area network (WPAN). Other targets in developing the 802.15.4 standard have been multi-year battery life and very low complexity. Low rate personal area networks (LR-WPANs) are used to transmit information over relative short distances. Unlike WLANs, connections effected via WPANs involve little or no infrastructure, which makes WPANs suitable for wireless sensor networks. Other potential applications are interactive toys, smart badges, remote controls and home automation. IEEE standard 802.15.4 (2007) enumerates the following features for LR-

WPAN:

Over the air data rates of 250 kb/s, 100 kb/s, and 20 kb/s

Two addressing modes: 16-bit short and 64-bit IEEE addressing

Support for critical latency devices

CSMA-CA channel access

Automatic network establishment by the coordinator

Fully handshake protocol for transfer reliability

Power management to ensure low power consumption

16 channels in the 2.4 GHz ISM band, 10 channel in the 915 MHz band and one channel in the 868 MHz band.

The ZigBee specification is based on the IEEE 802.15.4 standard.

IEEE Draft Standard 802.15.4a

The IEEE 802.15.4a Task Group (TG4a) has defined an alternative physical layer

(PHY) standard (IEEE Draft Standard 802.15.4a/D4, 2006) to the 802.15.4 standard.

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The main interest of TG4a has been in providing the following capabilities over the existing 802.15.4 standard:

high precision ranging and positioning (1 meter accuracy or better)

higher data rate than 802.15.4

ultra low power consumption

low cost

scalability to different data rates

longer range than in 802.15.4

The IEEE standard 802.15.4a (2006) specifies two optional signaling formats. The first signaling format, which is based on impulse radio UWB, can use 250-750 MHz,

3.244-4.742 GHz or 5.944-10.234 GHz frequency bands. The other signaling option, which is based on chirp spread spectrum (CSS), uses the 2.4-2.4835 GHz band. Only the UWB option contains an optional ranging capability, whereas the CSS signals can be used only for communication (Sahinoglu & Gezisi, 2006).

Ranging Protocols in 802.15.4a

There are three different ranging protocols defined in the IEEE 802.15.4a standard

(Sahinoglu & Gezisi, 2006):

mandatory two-way ranging protocol

optional symmetric double-sided two-way ranging protocol

optional private ranging protocol

These ranging protocols are illustrated in Figure 24.

The mandatory ranging protocol is based on the simple two-way ranging concept

illustrated in section 4.4.2. The mandatory ranging protocol consists only of the

transmission of the messages D2, A2, D4 and A4 in Figure 24. First, the originator

device sends a range request message D2 and the recipient device replies with the acknowledgement A2. The recipient also transmits a timestamp message D4. Finally, the originator sends an acknowledgement, A4, for the timestamp (Sahinoglu & Gezisi,

2006).

The optional symmetric double-sided two-way ranging protocol is illustrated with

messages D2, A2, and D3 in Figure 24. Addition of D3 to the simple double-sided two

protocol implements the symmetric protocol. As explained in section 4.4.2, performing

94 the two-way ranging symmetrically reduces the accuracy requirements of the crystals in the originator and recipient devices.

Figure 24. Illustration of the ranging protocols supported by the IEEE 802.15.4a standard (Sahinoglu & Gezisi, 2006)

The optional private ranging protocol makes the UWB ranging more secure against hostile attacks. The private ranging is implemented by adding the authentication step

consisting of the messages D1 and A1 before the ranging steps, as illustrated in Figure

24. The authentication process and encrypting the contents of the messages are

explained in (Sahinoglu & Gezisi, 2006).

7.4 Fusing Positioning Technologies

There is no single location technology that may be relied upon in all environments to provide accurate location information. Some technologies are designed to work best indoors while some technologies have better outdoor accuracy and global availability.

However, there is a need for a location-based service which would provide position

95 information both indoors and outdoors. In other words, the positioning device or service would change the underlying positioning technology seamlessly when the user moves between the outdoor and indoor environments. As an example, the user’s handset uses UWB or WLAN signals indoors for position determination. On the other hand, when the user walks out of the building the handset switches to use GPS signals.

In addition to combining outdoor and indoor positioning systems, there is also a need to fuse different indoor positioning technologies. For example, Aeroscout recently introduced a positioning system which combines the WLAN-based positioning system with UWB positioning. In general this system relies on WLAN positioning, but certain areas that call for very high accuracy can be equipped with UWB receivers. When the target device is in the coverage of UWB network, both WLAN and UWB signals are utilized in position determination and improved positioning accuracy is achieved.

(Aeroscout, [Cited April 23, 2008].)

Positioning systems provide their information in very different formats and offer different level of accuracy. Borriello & Deshpande (2002) present a concept called

Universal Location Framework, which fuses location technologies together. In this concept positioning technologies can be used either independently from each other, or the measurements obtained from them can be combined with each other. To make the position technology fusion easier, a layered software engineering model has been proposed. This model utilizes the common design principles identified in all positioning systems (Hightower & al., 2002).

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8 Conclusions and Discussion

This chapter discusses the results of the literature research made in this thesis and presents the conclusions. First, an insight to the markets of the local positioning systems is given. Second, the accuracy and availability requirements of different location-based services are discussed. Third, the signal technologies investigated in this thesis are compared. Finally, the conclusions of the thesis are summarized and future research topics are suggested.

8.1 Cost and Market Issues

The current local positioning systems are still relative expensive to purchase and maintain. For example, in Legoland Billund the parents can track the movement of their children inside the amusement park by using location service based on Aeroscout

TDOA estimation technology. The investment for a location system similar to

Legoland amusement park is between 100 000 € and 150 000 € (InformationWeek,

2004). Also the software-based systems may be expensive. A location fingerprinting system using WLAN RSS measurements for a hospital may cost 300 000 $ (Helsingin

Sanomat, 2008).

Because the current local positioning systems are quite expensive, the potential applications are still limited to the markets where relative valuable assets or wealthy people are tracked. Today, healthcare is the most important market segment of the indoor positioning systems (Helsingin Sanomat, 2008). Most of the current indoor positioning systems are used to track people and assets in hospitals, especially in the

United States. USA spends more money per capita on healthcare than any other nations (Medical News Today, 2005). Because of the huge amount of money in the healthcare segment, USA produces and consumes over 50% of world’s output medical technology (US Department of Commerce, 2007). Obviously, the American hospitals are good business for the location services and positioning equipment vendors.

Investing in the local positioning system may be financially justified also in amusement parks, because of the huge amount of potential users. There are 1.6 million visitors annually in Legoland Billund. The investment is paid back within a year if 2 % of the visitors rent a WLAN tag and pay 5 € rent. However, tracking children with

98 location-based systems at kindergarten or elementary school for safety reasons is probably still too expensive for the society. Even though human life is irreplaceable and any investment for the safety can be considered justified. However, in kindergarten and school environment the safety of the kids is improved better if the money is spent for hiring new personnel instead of purchasing child tracking technology.

In addition to the hospitals and amusement parks, there are some other potential market segments for the local positioning systems. Shopping malls, warehouses and industrial automation have been mentioned as examples of market areas where the local positioning technology can be applied (Aeroscout, [Cited January 24, 2008]).

According to (Helsingin Sanomat, 2008), improvement of safety in dangerous environments by utilizing the position information, is one potential application.

There are also outdoor applications, where the use of the local positioning technology is beneficial. Examples of these applications are various kinds of systems measuring the environment. For example, nodes of the wireless sensor networks must be very inexpensive and energy efficient. On the other hand, wireless sensor networks are used in military applications and the use of the C/A code receivers is not allowed for US military (CJCSI 6130.01C, 2003). Use of P(Y) code receivers in sensor network nodes is too expensive. In some systems, the position of the sensor node is tracked by measuring the AOA or TDOA of the sensor node transmission at the base stations and no extra hardware is needed at the sensor node. Using a local positioning system is financially justified, if a huge number of expendable sensor nodes are being used.

However, it can be anticipated that the cost of the local positioning system will become smaller in the future, when the amount of sold systems will be increased.

Most of the local positioning systems used today are network centric. If networkcentric systems are intended to locate persons, the privacy issues are raised. When position of the people is tracked, consent is always required. The markets of the indoor and outdoor location-based systems will not develop, if people do not have trust in the privacy of the location information.

8.2 Accuracy and Availability Requirements of Location-Based Services

The driving factor for the development of indoor GPS positioning techniques and cellular network-based positioning methods has been the United States Federal

Communications Commission (FCC) E911 docket on emergency call positioning in the USA (Dedes & Dempster, 2005). The FCC-E911 docket mandates that the

99 accuracy requirement for the handset-based positioning is 67% circular error probability (CERP) within 50 m and 95% CERP within 150 m. For the cellular network-based positioning the accuracy requirements are 67% CERP within 100 m and 95% CERP within 300 m.

These accuracy requirements are rather challenging both for the cellular positioning techniques and for the indoor GPS techniques. According to the tests made in (Singh,

2006a) the accuracy of current HSGPS receivers can be worse than 50 m in many indoor facilities. Also the accuracy of the cellular network-based positioning systems is usually worse than 100 m (Syrjärinne, 2001).

In spite of the extensive research on cellular network-based positioning techniques, these systems are mainly used on applications used by the emergency and law enforcement authorities. There are only few widely used commercial location-based services using the cellular network-based positioning technologies. The most important reason for this is the insufficient accuracy of current technologies. Other reasons are the lack of privacy due to the network centric approach and fees to be paid to the telecommunications operators.

The accuracy requirements for the most location-based services are higher than those set by USA and European emergency call mandates. For vehicle navigation or positioning people and objects inside buildings 1-10 meters accuracy is usually required to guarantee correct guidance and information to the user. Positioning accuracy of 50 meters is not enough to help a user to find a way to a correct address in urban areas (Syrjärinne, 2001, p. 10) or locate assets in hospitals or warehouses.

Typically, the accuracy level of GPS in good conditions is required for most of the outdoor location-based services. At indoor applications, granularity of room level or better is typically required.

In addition to accuracy, the availability of the positioning service is also an important

property for a location-based application. Figure 25 shows the availability and

accuracy of various positioning technologies. The GPS signals are available globally, but their accuracy starts to diminish at indoor facilities. On the other hand, the cellular network-based technologies can provide positioning capability for both indoor environments and very large geographical areas, but the accuracy is not sufficient for most of the applications. On the other hand, WLAN-based systems provide moderate accuracy in places where the access point network is dense, such as inside office buildings, but the coverage is limited to the campus areas or city centers. UWB technology would provide the best accuracy, but its outdoor use is limited.

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Figure 25. Comparison of positioning technologies in terms of accuracy and availability. Accuracy and availability numbers for GPS and cellular network-based systems are obtained from (Syrjärinne, 2001, p. 46).

Figure 25 shows that there is no positioning technology which would work both inside

buildings and in large geographic areas and provide 1-10 meters positioning accuracy.

When accuracy and availability are considered, there is a market segment for local position systems in environments where the location finding is performed at least partially indoors.

8.3 Comparison of the Signal Technologies

In this thesis the most common signal technologies were investigated. These signal

technologies and their characteristics are summarized in Table 2.

The most promising positioning technologies used in the local positioning systems use radio frequency (RF) electro-magnetic waves. The RF signal is used for the positioning from the same reasons as it is used for communications. The RF signal can penetrate walls and it has a wider communications bandwidth than ultrasound or infrared signal. Most of the current and emerging indoor positioning technologies are based on WLAN or UWB signals or proximity measurements using RFID technology.

All the other signal technologies have significant drawbacks when applied to local positioning systems. The infrared-based systems suffer from short range transmitters

101 and the huge amount of additional hardware (Rerrer & Kao, 2005). Also the ultrasound-based systems have drawbacks. Ultrasound pulses do not penetrate the walls and the pulses are easily interfered. In addition, a lot of hardware is needed to build the positioning infrastructure. Sensors used in dead-reckoning applications, such as accelerometer, gyroscope and magnetic compass, are practical in applications where the calibration to the local frame of reference is not a problem. In some applications, utilizing the motion tracking sensors may be justified when they are used in conjunction with radio frequency range observables.

Computer vision systems can determine the location of an object with sub-centimeter accuracy from analysis of video images. They are designed to capture position well in a single room immersive environment. However, computer vision systems are not usually designed to be scalable, not even to a building level.

Table 2. Summary of positioning technologies. Accuracies are obtained from

(Muthukrihnan & al., 2005; Hightower & Borriello, 2001)

Technology

Infrared-based

Vision-based

Ultrasound-based

Accuracy Availability Location estimation

5-10 m Indoors/ building

Proximity

1-100 cm Scene analysis

1-10 cm

Indoors/ room

Indoors/ building

Satellite-based

Cellular networkbased

WLAN-based

UWB-based

5-10 m

(20-50 m indoors)

Global

50-300 m Rural

2-100 m

6-10 cm

Campus/ indoors/ outdoors

Indoors/ building

Examples

Active Badge

Easy Living

TOA trilateration, proximity Active Bat, MIT

Cricket

TOA/ trilateration

RSS, AOA, TDOA/ proximity, triangulation

GPS,

Galileo,

GLONASS

Cellular phone localization

TDOA lateration, location fingerprinting

TOA/TDOA trilateration

Aeroscout,

Ekahau

Ubisense

8.4 Conclusions

In this thesis various technologies for local positioning systems were investigated.

Table 3 summarizes the merits and drawbacks of the positioning technologies

discussed in this thesis. The positioning technologies based on radio frequency signals were found most promising.

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Table 3. Merits and drawbacks of the positioning technologies in local positioning system context (Muthukrishnan & al., 2005).

Technology

Infrared-based

Vision-based

Merits

Compact

Low power

Simple

High accuracy

Ultrasound-based Measurement of TOA is easy (sound propagates slowly)

High accuracy

Simple

Satellite-based

(HSGPS, AGPS)

No need to build the positioning infrastructure

Global scale

Mature technology

Cellular networkbased

Existing infrastructure can be used for positioning and communications

Very wide scale

WLAN-based

UWB-based

Moderate accuracy

High bandwidth for communications

Existing communications infrastructure can be used for positioning

Based on mature technology

High accuracy

Less affected by multipath

Drawbacks/remarks

Range is typically less than 5 m

Restriction to line of sight conditions

Unusable in direct sunlight

Restriction to line of sight conditions

Unusable in direct sunlight

Range is less than 10 m

Vulnerable due to external interference

Multipath

Low accuracy and availability at indoor facilities

Low accuracy

Operator fees

Range is between 50 and 100 m

Multipath is a problem in TDOA systems

Radio map has to be calibrated in LF systems

Not mature technology yet

Smaller range than in WLAN

Currently, WLAN-based systems are very popular in indoor positioning. It has been estimated that the market share of the WLAN-based systems will be at least one third of the indoor positioning markets (Helsingin Sanomat, 2008). One of the most important advantages of WLAN-based positioning techniques is that WLAN communications technology is mature and well-developed. WLAN devices are standard and rather inexpensive. Existing infrastructure built for communications purposes can be used also for positioning purposes, if the system uses RSS measurements for location estimation. Thus no deployment of external hardware is needed. Also the existing communication channel can be used for delivering the measurement data and position solutions between the users and network servers.

There are two commercial positioning technologies based on the WLAN signals. The other systems measure the RSS and estimate the location of the mobile device by the

103 location fingerprinting method (Ekahau, [Cited January 24, 2008]), while the other systems measure the TDOA and compute location by trilateration (Aeroscout, [Cited

January 24, 2008]). The RSS-based systems can utilize the existing wireless communications system built using standard WLAN equipment. However, these systems based on location fingerprinting have the drawback that the radio map has to be built before use by measuring the signal strengths from the whole target area. On the other hand, the offline phase calibration is not needed in the TDOA-based systems, but special receivers capable of TDOA measurements are needed. Also, the base stations have to be synchronized precisely in time. The TDOA approach works well in open areas where the dominant direct path (DDP) channel condition is typical. On the other hand, in heavily multipath environments where the undetected direct path (UDP) channel condition is common, the location fingerprinting approach performs better than TDOA approach (Hatami & al., 2006). Thus, the location fingerprinting approach may be a better choice for office and hospital environment and TDOA approach for open areas, such as sport halls. Neither of these WLAN-based techniques is feasible, if better than 1–2 meters accuracy is required.

When the accuracy is important UWB is the most interesting indoor positioning technology. UWB provides a great ranging accuracy due to the very short pulse from which the arrival time of the signal is obtained. In addition, UWB is less affected by the multipath than WLAN or indoor GPS. On the other hand, limited range of the signal is a problem of current UWB. Opposite to WLAN, UWB is not a mature technology yet. One of the main problems in UWB technology is the complexity of the current arrival time measurement schemes due to the high sampling rate needed. A lot of development will be still needed before competitive UWB-based indoor positioning systems appear to the market. USA has recently approved using of UWB for public applications, while in Europe the legislation and standardization are not yet ready.

UWB transceivers are still quite expensive, but their prices may come down when the volumes of manufactured components rise.

Obviously GPS (or another GNSS) is the best candidate for positioning in outdoor environment. Use of GPS should be considered also in combined indoor and outdoor positioning applications where only building-level accuracy is needed when the user is indoors. Thus, high sensitivity GPS receiver with assistance information is a good choice for cellular phones. GPS is also the best technology for many wireless sensor applications measuring the environmental parameters outdoors. However, if the number of sensor units is very high, deploying a local position system may be beneficial.

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8.5 Future Research

The market of the local positioning systems is still quite small, mainly because the purchasing prices and the maintenance costs of these systems are relatively high. In addition, the current local positioning systems should be more accurate, more scalable and more reliable before they could be used more widely. A lot of research is still needed to improve the existing positioning technologies and to develop new ones, before the consumer and industrial markets will open for the local positioning systems.

Two potential future research topics are proposed in this section. First, utilization of the redundant measurements in location fingerprinting method is discussed. Second, the importance of fusing local positioning techniques with GPS is emphasized.

Improvements to the Location Fingerprinting Method

According to Kaemarungsi (2005) increasing the number of access points in location fingerprinting method improves the probability of returning the correct location. This is intuitive and it holds also to the navigation systems using TOA or AOA measurements. Increasing the number of base stations improves the geometry.

Additionally, the extra observations make the effect of noisy or incorrect observations smaller.

Not only has the number of access points affected to the performance, but also their mutual locations. If the access points are far away in the same direction from the research area, the fingerprints of the points in this area are probably close to each other. In spite of that the concept of dilution of precision (DOP) cannot be applied directly to the location fingerprinting, the geometry of the access points is important.

This has not yet been discussed in the research papers. Intuitively, the DOP value should be calculated for each point in the research area while examining the optimal locations of the access points.

Another issue related to the number of access points and geometry is exclusion of the faulty measurements. In GPS redundant measurements can be utilized to detect and exclude the faulty measurements from the navigation solution. RAIM algorithm is able to detect the presence of a faulty measurement if there are at least five measurements

(one measurement more than unknown variables). If there are at least six measurements, one faulty measurement can be identified and isolated from the solution

(Kaplan, 1996, p. 306). RAIM algorithm is based on linear algebra and thus not applicable in location fingerprinting. In location fingerprinting increasing number of

105 access points improves the redundancy, which should enable to detect and isolate faulty measurements. However, no papers were found where redundancy is utilized in excluding the faulty measurements. Utilizing the redundant measurements and applying the geometry (DOP) concept to the location fingerprinting method are suggested for a future research topic.

Fusing Positioning Technologies

None of the positioning technologies discussed above would have optimal performance for all possible applications and environments. Some technologies are designed to work best indoors while some technologies have better outdoor accuracy and global availability. However, there is a need for a location-based service which would provide position information both indoors and outdoors. Besides this, integrating different indoor positioning technologies together, such as WLAN and

UWB, would also open new interesting markets.

A service or a device which integrates GPS and some indoor positioning technology together would have the highest market potential. Obviously, cellular phone would be the most interesting user terminal for such positioning service. The important question is which indoor positioning technology should be integrated with GPS. Based on the conclusions above, the most potential alternative technologies would be based on

UWB or WLAN.

It is obvious that the indoor positioning technology to be integrated with satellite navigation should be based on open standards before it can be widely accepted in consumer and industrial markets. In practice this means that the base stations of the indoor positioning systems must support mobile terminals of different vendors and vice-versa. In other words, there is a similar need for standardization of the communication and ranging protocols in the indoor part of the fused positioning system as in cellular network positioning systems and in GPS. In addition, it is favorable that also the indoor positioning part of the fused system would be based on self positioning.

In spite of that the location fingerprinting of WLAN signals is currently the most potential technology used, it is more probable that the indoor component of the fused system will be based on TDOA or two way TOA schemas. Opposite to location fingerprinting, the TDOA and TOA systems can be designed as self-positioning systems. In the self-positioning approach the mobile terminal can make the decision whether to use GPS signals or indoor positioning system signals or both.

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At the moment it is not clear which local positioning technique would be the best alternative for the indoor component of the fused positioning system. WLAN is designed for communications purposes and there are no standards defined for ranging purposes. In principle it would be possible to define ranging standard also for WLAN.

However, arrival time measurements of WLAN signals suffer from multipath effect in many indoor conditions. On the other hand, UWB is more accurate in multipath environment and there is already IEEE draft standard 802.15.4a for high precision ranging and positioning. It is a drawback for UWB is that it has smaller range than

WLAN and it is intended rather for personal area than local area communications.

Which local positioning technology will be integrated with global positioning technologies, remains to be seen in the future.

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9 Summary

Markets of the location-based services, such as map-based guidance and navigation, intelligent transport services and tracking of people and valuable assets, are growing rapidly. This creates a promising area to be investigated further. Today most of these location-based services depend on GPS as a position data provider, but there is also a growing interest in positioning applications which are independent from GPS or other satellite-based navigation technologies. These positioning systems, which are designed to operate inside a limited geographical area, are called local positioning systems.

This research identified and analyzed state-of-the-art techniques for local positioning systems. The research also aimed to match the characteristics of the identified local positioning techniques to the needs of various location-based applications. At this stage it can be concluded that no single technology was found, which would have provided optimal performance for all possible applications and environments.

However, there clearly are a few promising methods which outperform certain others, depending on what the purpose of usage is.

The most widely used local positioning systems today are based on the use of WLAN signals. There are two WLAN-based methods that can be recommended – depending on where it is used. The technology based on location fingerprinting method was found the most promising technology in heavily multipath environment, such as office buildings. On the other hand, the technology based on measuring the WLAN signal propagation time performs better in large open areas, such as sport halls. Neither of these WLAN-based technologies is feasible, if better than 1–2 meters accuracy is required.

When it comes to accuracy instead, different techniques are recommended to be used, some of which are not perfect but will do. Based on this study, utilizing UWB signals for positioning would be an interesting alternative, especially when accuracy is important. UWB provides a great accuracy due to the very short pulse, from which the arrival time of the signal is obtained. However, opposite to WLAN, UWB is not a perfectly mature technology yet and the legislation process of UWB signals is still going on in Europe. As it develops, in the future UWB should be taken into account as a reasonably useful option in indoor positioning.

108

If only building level accuracy is required at indoor environment, use of the assisted

GPS is recommended. However, due to the multipath problems present in narrowband signal TOA estimation the satellite navigation systems cannot compete with local positioning technologies in navigation or asset tracking applications inside the buildings.

Several other positioning technologies have also been identified, which are not based on using radio frequency electro-magnetic waves like WLAN and UWB-based systems. For example in computer vision systems the location of an object can be derived with sub-centimeter accuracy from analysis of video images. It is obvious that this kind of technology would be most suitable for such applications where optical line of sight between the video camera and the target object can be guaranteed. One of considerable technologies is dead-reckoning method, which can only be recommended in applications where the calibration to the local frame of reference is not a problem.

Compared to computer vision and dead-reckoning systems, the RF-based local positioning systems are still more popular because of the better scalability and coverage.

Despite the significant research in the field of positioning, there are still numerous big challenges to be solved in local positioning systems and indoor navigation. Based on this study it can be anticipated that one of the most important research topics in the future would be fusing indoor positioning techniques with the satellite-based navigation systems. There will be remarkable market opportunities for a locationbased service, which can switch between the positioning technologies when the user moves between outdoor and indoor environments. In this research it is considered that the most probable technology to be integrated with GPS will be based on ranging approach of UWB or WLAN signals. Finally, another interesting future research topic will be better utilization of redundant measurements in location fingerprinting systems.

109

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