technical analysis of new paradigms

technical analysis of new paradigms
TECHNICAL ANALYSIS OF NEW
PARADIGMS INCREASING EGNSS
ACCURACY AND ROBUSTNESS IN
VEHICLES
EUROPEAN GNSS AGENCY
MAY 2015
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TABLE OF CONTENTS
1. INTRODUCTION .............................................................................................................. 9
1.1. METHODOLOGY ...................................................................................................... 9
2. EXECUTIVE SUMMARY ................................................................................................... 10
3. CURRENT ROLE OF EGNSS IN ROAD DOMAIN.................................................................. 12
3.1. STAND-ALONE GNSS RECEIVERS........................................................................... 13
3.1.1. MULTICONSTELLATION RECEIVERS .................................................................................................. 14
3.2. AUGMENTATION AND AIDING SYSTEMS TO EGNSS................................................. 14
3.3. HYBRIDIZATION WITH EXTERNAL SOURCES........................................................... 15
3.3.1. INTEGRATION WITH INERTIAL SENSORS.......................................................................................... 16
3.3.2. DEAD RECKONING .......................................................................................................................... 18
3.3.3. MAP MATCHING .............................................................................................................................. 19
4. POSITIONING PERFORMANCE FEATURES IN ROAD DOMAIN ............................................. 20
4.1. MAIN GNSS-BASED APPLICATIONS IN ROAD DOMAIN AND THEIR NEEDS ................ 20
4.1.1. ROAD APPLICATIONS SURVEY.......................................................................................................... 20
4.1.2. NEEDS FOR IMPROVING POSITIONING PERFORMANCE...................................................................... 23
4.2. POSITIONING PERFORMANCE FEATURES................................................................ 25
4.3. TECHNOLOGICAL ENABLERS ................................................................................. 29
5. TECHNOLOGIES SURVEY ............................................................................................... 30
5.1. EVALUATION CRITERIA ......................................................................................... 30
5.2. MULTIFREQUENCY RECEIVERS .............................................................................. 32
5.2.1. TECHNOLOGICAL ISSUES OF MULTIFREQUENCY RECEIVERS ............................................................. 33
5.2.2. PERSPECTIVES TOWARD THE ROAD SECTOR .................................................................................... 33
5.3. DEEP INTEGRATION WITH SENSORS ..................................................................... 35
5.3.1. COMBINATION OF GNSS RECEIVERS WITH VISION SENSORS ........................................................... 35
5.3.1.1. Visual information as an additional measurement in input to the navigation filter stage 36
5.3.1.2. Visual information at the INS stage ....................................................................... 36
5.3.1.3. Visual information to identify NLOS measurements .................................................. 36
5.3.1.4. Place recognition through visual information ........................................................... 37
5.3.1.5. Technological Issues of Combination with vision sensors .......................................... 37
5.3.1.6. Perspectives toward the road sector ...................................................................... 38
5.3.2. TIGHT INTEGRATION WITH IMU ....................................................................................................... 39
5.3.2.1. Fundamentals of TC integration............................................................................. 39
5.3.2.2. Technological Issues of Tight Integration with IMU .................................................. 41
5.3.2.3. Perspectives toward the road sector ...................................................................... 42
5.4. MITIGATION OF STRUCTURED INTERFERING SIGNALS............................................ 44
5.4.1. SPOOFING RISK .............................................................................................................................. 44
5.4.2. AUTHENTICATED CIVILIAN SIGNALS ................................................................................................ 45
5.4.2.1. Technological issues of signal authentication for road users ...................................... 46
5.4.2.2. Perspectives toward the road sector ...................................................................... 47
5.4.3. SPOOFING COUNTERMEASURES FOR STANDALONE GNSS RECEIVERS............................................... 48
5.4.3.1. Technological issues of spoofing countermeasures for standalone receivers ................ 49
5.4.3.2. Perspective towards the road sector ...................................................................... 49
5.5. INTEGRATION WITH SIGNALS OF OPPORTUNITY .................................................... 51
5.5.1. DIGITAL TELEVISION SIGNALS ........................................................................................................ 52
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5.5.1.1. Fundamentals of Digital Television Signals ............................................................. 52
5.5.1.2. Technological Issues of Digital Television Signals .................................................... 53
5.5.1.3. Perspectives toward the road sector ...................................................................... 54
5.5.2. TERRESTRIAL NETWORK-BASED SYSTEMS........................................................................................ 55
5.5.2.1. Fundamentals of Terrestrial Network-Based Systems ............................................... 55
5.5.2.2. Positioning in cellular networks ............................................................................. 56
5.5.2.3. Technological Issues of Positioning in Cellular Networks ........................................... 57
5.5.2.4. Perspectives toward the road sector ...................................................................... 57
5.5.3. DEDICATED SHORT RANGE COMMUNICATION (DSRC) TECHNOLOGY ................................................. 59
5.5.3.1. Fundamentals of DSRC ........................................................................................ 59
5.5.3.2. DSRC as signal of opportunity to improve GNSS performance ................................... 59
5.5.3.3. Technological Issues of combination of GNSS with DSRC.......................................... 60
5.5.3.4. Perspectives toward the road sector ...................................................................... 60
5.6. METHODS FOR PRECISE POSITIONING .................................................................. 62
5.6.1. PRECISE POINT POSITIONING (PPP) ................................................................................................. 62
5.6.1.1. PPP components ................................................................................................. 63
5.6.1.2. Error Mitigation in a PPP-enabled receiver .............................................................. 64
5.6.2. REAL TIME KINEMATIC (RTK) ........................................................................................................... 65
5.6.2.1. RTK fundamentals ............................................................................................... 66
5.6.2.2. Performance of RTK with high end receivers ........................................................... 67
5.6.2.3. First results of RTK with low cost receivers ............................................................. 67
5.6.3. TECHNOLOGICAL ISSUES FOR PRECISE POSITIONING FOR ROAD USERS ........................................... 68
5.6.4. PERSPECTIVES TOWARD THE ROAD SECTOR .................................................................................... 69
5.7. ADVANCED DIGITAL MAP MATCHING ..................................................................... 72
5.7.1. TECHNOLOGICAL ISSUES FOR ADVANCED DIGITAL MAP MATCHING .................................................. 73
5.7.2. PERSPECTIVE TOWARD THE ROAD SECTOR ...................................................................................... 73
6. CONCLUSIONS AND RECOMMENDATIONS....................................................................... 75
7. REFERENCES ................................................................................................................ 78
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LIST OF TABLES
Table 3.1: IMUs classification. ...................................................................................... 16
Table 3.2: Sensors commonly used as complement to GNSS receivers for enhancement of incar navigation systems (from [RD27]). .................................................................... 18
Table 4.1: List of possible application classes (from [RD37]). ........................................... 22
Table 4.2: Definition of the positioning performance features ........................................... 27
Table 4.3: Correlation between the application needs, gathered in terms of criticality, and the
performance features. ............................................................................................ 28
Table 4.4: Correlation between the performance features and the identified technological
enablers. .............................................................................................................. 29
Table 5.1: IGS product table. ....................................................................................... 64
Table 5.2: Typical error budgets for different types of signal processing. ........................... 67
Table 5.3: Horizontal Position Error reported in [RD100]. ................................................ 68
Table 6.1: Overall evaluation of the identified technological enablers for the performance
features. .............................................................................................................. 77
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LIST OF FIGURES
Figure 3.1: Examples of GNSS chipsets produced by different manufacturers (Qualcomm,
Broadcom, Mediatek, u-blox, SiRF, STMicroelectronics, and NVS) ............................... 13
Figure 5.1: Typical scheme for a tightly integrated GNSS/INS system .............................. 39
Figure 5.2: Comparison between GNSS standalone positioning (blue) and TC integration
between GNSS and IMU (red). (a) passage through a city tunnel and (b) parallel street
lanes with trees in the surrounding ......................................................................... 40
Figure 5.3: Example of Duty-Cycle tracking scheme ....................................................... 46
Figure 5.4: Digital terrestrial television systems worldwide (source: [RD80]). ................... 53
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ACRONYMS
2G
Second Generation Mobile
3G
Third Generation Mobile
4G
Fourth Generation Mobile
ACC
Adaptive Cruise Control
ADAS
Advanced Driver Assistance System
AGNSS
Assisted GNSS
AOA
Angle Of Arrival
ATSC
Advanced Television System Committee
BS
Base Station
CEN
European Committee
Normalisation)
CEP
Circular Error Probability
COTS
Commercial-On-The-Shelf
CS
Commercial Service
DR
Dead Reckoning
DSRC
Dedicated Short Range Communication
DT
Digital Tachograph
DTMB
Digital Terrestrial Multimedia Broadcasting
DVB-H
Digital Video Broadcast – Handheld
DVB-T
Digital Video Broadcast – Terrestrial
DVB-T2
Digital Video Broadcast – Second Generation Terrestrial
EC
European Commission
ECEF
Earth-Centred-Earth-Fixed
EDAS
EGNOS Data Access Service
EFC
Electronic Fee Collection
EKF
Extended Kalman Filter
EGNOS
European Geostationary Navigation Overlay System
EGNSS
European GNSS (encompassing Galileo and EGNOS)
ETSI
European Telecommunications Standards Institute
EU
European Union
FP7
Seventh Framework Programme
GBAS
Ground Based Augmentation System
GDOP
Geometric Dilution of Precision
GNSS
Global Navigation Satellite System
GSA
European Global Navigation Satellite Systems Agency
GSM
Global System for Mobile Communications
HMT
Hazardous Material Tracking
ICBM
InterContinental Ballistic Missiles
ICT
Information and Communications Technologies
for
Standardization
(French:
Comité
Européen
de
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IGS
International GNSS Service
IMU
Inertial Measurement Unit
INS
Inertial Navigation System
ISDB-T
Integrated Services Digital Broadcasting-Terrestrial
ISO
International Organization for Standardization
ITS
Intelligent Transport Systems
IVS
In-Vehicle System
LAAS
Local Area Augmentation System
LBS
Location Based Service
LC
Loosely Coupled
LCA
Liability Critical Applications
LiDAR
Light Detection and Ranging
LIS
Location Insight Services
LTE
Long Term Evolution
M2M
Machine-to-Machine
MAC
Medium Access Control
MCA
Multi-Criteria Analysis
MEMS
Micro Electro-Mechanical Systems
MFN
Multi-Frequency Network
MM
Map Matching
MPS
Minimum Performance Standard
NCA
Non-Critical Applications
NMA
Navigation Message Authentication
NME
Navigation Message Encryption
OBU
On-Board Unit
OFDM
Orthogonal Frequency Division Multiplexing
OS
Open Service
OTD
Observed Time Difference
OTDOA
Observed Time Difference Of Arrival
PAYD
Pay As You Drive
PND
Personal Navigation Device
PPP
Precise Point Positioning
PPUI
Pay Per Use Insurance
PVT
Position, Velocity, and Time
RFID
Radio Frequency IDentification
RSS
Received Signal Strength
RTK
Real Time Kinematic
RUC
Road User Charging
SBAS
Satellite Based Augmentation System
SCA
Safety Critical Applications
SCE
Spreading Code Encryption
SFN
Single Frequency Network
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SNR
Signal-to-Noise Ratio
SoO
Signals of Opportunity
SQM
Signal Quality Monitoring
TC
Tightly Coupled
TDOA
Time Difference Of Arrival
TOA
Time Of Arrival
TRL
Technological Readiness Levels
TTFF
Time To First Fix
TV
Television
UHF
Ultra High Frequency
UMTS
Universal Mobile Telecommunications System
UTC
Universal Time Coordinated
UWB
Ultra-WideBand
V2I
Vehicle-to-Infrastructure
V2V
Vehicle-to-Vehicle
VHF
Very High Frequency
WAAS
Wide Area Augmentation System
WG
Working Group
Wi-Fi
Wireless Fidelity
WLAN
Wireless Local Area Network
WP
Work Package
WSN
Wireless Sensor Network
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1. INTRODUCTION
The scope of the document is

To provide a short but comprehensive review of the technologies currently in place in the
field of EGNSS as applied to the road domain (Section 3), focusing on the stand-alone
GNSS receivers, the role of augmentation and aiding systems to GNSS, then
complementing with the hybridization with external sources.

To report on the analysis of the positioning needs for some identified main classes of road
applications (Section 4).

To provide the results of an investigation of the main technological enablers (Section 5),
considering potential advance beyond the state-of-the-art.

To summarise some major outcomes of the analysis (Section 6).
It’s worth mentioning that the content of this document doesn’t consider in detail the
“autonomous vehicle” topic.
1.1. METHODOLOGY
The organisation itself of this document reflects a three-step analysis approach, specifically:
1. Identify the current technological framework in the field of EGNSS applied to ITS in the
road domain, with the aim to recognize the state-of-the-art from three different
perspectives:
-
the stand-alone GNSS receiver, part of the in-vehicle positioning module of a generic
positioning system for road applications;
-
the aiding and augmentation data provided by external systems (and received by the
GNSS receiver) complementing the information carried over the satellite signals;
-
the integration of GNSS receiver with other in-vehicle technologies, e.g. inertial
sensors.
2. Identify the positioning needs of the major classes of road applications.
3. Investigate the potentiality of some technologies (beyond the state-of-the-art) in
effectively enabling the expected positioning performance.
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2. EXECUTIVE SUMMARY
The road sector is a major potential market for GNSS applications and satellite navigation
receivers are now commonly installed in new cars as a key tool for providing new services to
the drivers. As a matter of fact, GNSS is the primary source of in-vehicle positioning in road
transportation. Most of in-vehicle positioning systems embed mass-market stand-alone GNSS
receivers (see Section 3.1), generally processing only GPS civil signals over the L1 band.
However, the current trend is to include other constellations (e.g. GLONASS and Galileo)
broadcasting signals on the same band, in order to increase positioning availability and
accuracy in conditions of limited sky visibility. These GNSS receivers are typically able to
accept differential corrections broadcast by Satellite Based Augmentation System (e.g.
EGNOS) and they are often enabled for Assisted-GNSS (see Section 3.2). Several error
sources still deteriorate the quality of the position and velocity estimates of a GNSS receiver,
in particular in an urban scenario (i.e.: multipath, atmospheric effects, poor satellite
geometry due to limited sky visibility, shadowing, etc.). To overcome this limitation, data
from existing on-board sensors, either motion sensors (e.g., steering encoder, odometer,
wheel velocity encoders) or inertial sensors (e.g., accelerometers and gyroscopes), are
loosely integrated with GNSS receivers (see Section 3.3).
Nonetheless, the expansion of terrestrial applications and new Location Based Services (LBS)
has fostered the design of complex location systems to comply with the increasingly
demanding needs of these applications. Such a complexity depends on the type of the target
service, ranging from a simple position reporting (e.g. in the case of a low-end asset
management) to the provision of a “reliable” data (e.g. authenticated and with a mastered
uncertainty) on the vehicle’s trajectory, mainly for liability-critical applications (Section 4).
Starting from this context, we analysed the main GNSS-based applications in the road
domain, gathering them in a set of few classes, each of which referring to some key functions
(see Section 4.1.1). Then, for each class of applications, we identified the needs in terms of
positioning performances (e.g. robustness, increased accuracy, etc., see Section 4.1.2), that
have been mapped to specific performance features (in Section 4.2).
Then we selected a number of “technological enablers” beyond the state-of-the-art that
potentially could achieve the expected positioning performance identified in Section 4.3. A
deep investigation (see Section 5) of all such technologies from different relevant
perspectives (technological maturity, complexity, costs, etc.) allowed pointing out the most
promising paradigms in order to increase the GNSS accuracy and robustness in future road
applications.
The outcomes of the performed analysis (Section 6) show that:

The implementation of new schemes for fusing data from GNSS and inertial sensors at low
level (see Section 5.3.2) represents a cost-effective solution able to support most of the
performance features (e.g. accuracy, authentication, integrity). This is especially true if
such schemes are complemented with multi-frequency receivers (Section 5.2).

The precise positioning techniques (i.e. PPP and RTK, see Section 5.6) on top of
measurements taken with mass market GNSS receivers come with a quite low TRL today,
but the availability of new satellites from new constellations (e.g. Galileo) will encourage
the research on this topic, as it will increase the possibility to use carrier phase
measurements also in challenging environments like urban.

When available (e.g. vehicles equipped with a dedicated OBU as for the DSRC), the SoO
represent a good solution in complementing EGNSS, mostly for the purposes of position
authentication, since they are non-GNSS technologies (see Section 5.5). Their
contribution for other features is not yet mature, but DSRC – as soon as some limitations
in the quality of measures will be overcome - appears a promising localization technology
to look (see Section 5.5.3).

Current low cost video-cameras provide a very limited added value in terms of
performance improvements. On the other hand expensive devices such as LiDAR are
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strongly limited due to their constraints in terms of cost (see Section 5.3.1). An effective
support to the positioning performance, accuracy at first, is expected from high-end
sensors, but comes with medium-high cost. These expensive sensors are typically used in
specific demanding applications (e.g. autonomous driving).
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3. CURRENT ROLE OF EGNSS IN ROAD DOMAIN
GNSS is the today primary source for vehicles positioning and is currently used for different
purposes:

Navigation, possibly with real-time traffic information (e.g. with a portable navigation
devices);

Real time monitoring for an efficient fleet management, with the final objective of
improving the logistic quality of service;

Supporting measurements to assess off-line the quality of the delivered services. This
in turn enables Location Insight Services (LIS) to provide valuable statistic for
economic analysis;

Geo-localization information collected during operations.
Nowadays, different types of positioning sensors can be used to determine the position and
velocity of a vehicle. In road transportation, GPS is the primary source of positioning, but
other technologies are often used to complement standalone receivers. Indeed, several error
sources deteriorate the quality of the position and velocity estimates of a GNSS receiver, in
particular in a urban scenario (i.e.: multipath, atmospheric effects, poor satellite geometry
due to limited sky visibility, shadowing, etc.), making stand-alone receivers often unsuitable
as sole-mean navigation equipment for many applications.
Today, multi-constellation receivers are quickly becoming the state of the art (e.g.: receivers
able to process GPS, Galileo and GLONASS signals transmitted over the same frequency, i.e.
the L1/E1 band). They are able to process new GNSS signal formats and can be integrated
with inertial sensors and odometers to improve positioning performance. These aspects will
be briefly presented in this section.
It is worth to point out that the combination of GNSS receivers with video cameras
(application rising interests in the context of precise agriculture) is still rather complex, and
developed in the frame of research projects [RD01]. Although video cameras are more and
more present on board of the vehicles, they are principally intended for the detection of near
obstacles (Driver Assistance Systems, DAS, e.g., park assistance, collision avoidance, lane
departure warnings, pedestrian protection) and in some case for road sign recognition, but
their integration with the positioning system cannot be considered state of the art
[RD02][RD03][RD04][RD05][RD06][RD07]. One of the current barriers to the active
integration of visual information into the positioning system is the need of augmenting the
digital maps with detailed visual information, which would dramatically increase the size of
the digital maps and the complexity of the signal processing.
A similar situation holds for the integration between GNSS and radar or Light Detection and
Ranging (LiDAR) measurements [RD08]: although the use of LiDAR can be used to precisely
detect obstacles along the road, and its integration with the GNSS receiver has been
demonstrated to give benefits, the application is still limited to proof of concepts (e.g.,
prototyping and demonstration of autonomous vehicles [RD09]), mainly due to the associated
cost of the LiDAR equipment [RD07].
In what follows we present a short review of the technology currently in place in the field of
EGNSS applied to ITS in the road domain. We develop the analysis starting from the standalone GNSS receivers, then we move to consider aiding and augmentation systems, which
complement the data from the satellite constellation with other information generated by
external systems. We conclude with discussing the current level of integration of the
commercial EGNSS receivers for vehicular use with other technologies on board of the
vehicles, in particular motion sensors and low-cost Inertial Measurement Units (IMUs).
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3.1. STAND-ALONE GNSS RECEIVERS
Today, most of positioning systems for road applications embed mass-market GNSS
receivers. Generally, they are single frequency and process GPS signals over the L1 band
(i.e.: 1575.42 MHz). They work standalone or are loosely coupled with other on-board
sensors. The trend for this type of receivers is to include other constellations broadcasting
signals on the same band, in order to increase positioning availability and accuracy in
conditions of limited sky visibility [RD10].
Nowadays about ten manufactures producing GNSS chipsets for mass-market applications are
present, while several companies embed these components in GNSS modules to be more
easily integrated in final products. Figure 3.1 shows the newest products from the main GNSS
mass-market chips manufacturers. They are Qualcomm, Broadcom, Mediatek, u-blox, CSR
and STMicroelectronics. Beside these producers it is worth to mention NVS Technologies AG
that produces GNSS chipsets and modules that are appreciated for their level of flexibility, at
a moderated cost. CSR (SiRF series) and STMicroelectronics (Teseo series) are also well
known and used in road and Machine-to-Machine (M2M) applications.
Figure 3.1: Examples of GNSS chipsets produced by different manufacturers (Qualcomm, Broadcom,
Mediatek, u-blox, SiRF, STMicroelectronics, and NVS)
In general, the price of these GNSS chips is less than ten EUR, while the price of GNSS
modules is between twenty and forty EUR for small quantities. The difference between chips
and modules consists in the fact that the first are usually embedded in large production
devices (e.g. smart phones) while the seconds are easy to integrate and fit better for small
productions with looser size constraints.
Current mass-market receivers are seldom “purely stand-alone” receivers, as they are
typically able to accept differential corrections broadcast by Satellite Based Augmentation
System (SBAS) over the L1 band (see Section 3.2 Augmentation and aiding systems to
EGNSS), often enabled for Assisted-GNSS (A-GNSS) (Section 3.2) and sometimes ready for
multi-constellation operations (see Section 3.1.1 Multiconstellation receivers).
Although their nominal accuracy performance are typically below 1.5 m without any external
aiding, the actual performance principally depend on the surrounding environment, namely
sky visibility, multipath and non-line-of-sight propagation, interference, as well as on the
vehicle motion. Actual performance may be dramatically impaired from a harsh environment.
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3.1.1. MULTICONSTELLATION RECEIVERS
Multi-constellation receivers address the issue of scarce satellite visibility in urban
environments by enabling concurrent access to different GNSS systems. This means that
even when the receiver can only see a narrow sky slice, the probability of having enough
visible satellites is doubled. In this way, visibility improves since more satellites are likely
visible in the non-blocked portions of the sky [RD12].
3.2. AUGMENTATION AND AIDING SYSTEMS TO EGNSS
GNSS augmentation systems were born to continuously provide robust and safe
navigation, especially when high precision or enhanced coverage or availability is required.
Accuracy, availability, integrity, and continuity are the key performance of any GNSS, so that
procedures and external aids to improve them have been developed under the label of the
augmentation systems. Augmentation systems attempts to correct for many of the dominant
error sources in GNSS. It is basically accomplished by applying several types of corrections to
the user’s receiver in order to improve its performance. These corrections are typically
computed by a reference station at a precisely known location (or by multiple connected
reference stations). Augmentation works only against common mode, spatially correlated
errors such as the ionosphere and troposphere delays. Multipath induced errors, as well as
interference-induced ones, are not common to the reference station and the user; therefore
they cannot be recovered by means of any augmentation system [RD13].
Current mass-market GNSS receivers are typically able to accept differential corrections
broadcast by Satellite Based Augmentation System (SBAS) over the L1 band [RD14]
(e.g., from EGNOS or WAAS), while the commercial (i.e., not open-access) augmentation
systems like Omnistar, Starfire, Veripos, Seastar require a receiver setup which is still limited
to specific professional applications with high precision requirements and the availability to
sustain a subscription fee [RD15].
According to the authors experience gained through practical tests, EGNOS corrections
applied to mass-market GPS receivers bring to an improvement equal to 30% on the
positioning accuracy, assuming open sky conditions. However, as demonstrated by some
measurement campaigns, the applicability of EGNOS corrections and integrity information
shows some limitations in vehicular scenarios, especially in urban canyons [RD16]. It must be
noticed that the availability of EGNOS messages from an alternative source, as the EGNOS
Data Access Service (EDAS), can potentially reduce this problem [RD17].
Another widely adopted technology in GNSS mass-market receivers, especially embedded in
smartphones and car navigators, is the Assisted-GNSS (AGNSS) [RD18]. It is well known
that most of today's LBS use GPS receivers that, considering the present performance of
Component Off The Shelf (COTS) devices, normally take around 30 sec to fix the user's
location, because they need to download ephemeris and almanac information from the
satellites (in cold start conditions). However, under critical signal in space conditioning, the
receiver could take minutes to provide useful and reliable information. This is, of course, a
very limiting factor for LBS that in most cases need a response time of around a few seconds.
AGNSS techniques (especially Assisted-GPS – AGPS) have been designed in past years for
two main purposes: to reduce the Time To First Fix (TTFF) and to increase the sensitivity of
the receiver in harsh environments (e.g., indoors and urban canyons). The core idea is to
provide assistance data to the terminal via a wireless network. Such aids include but are not
limited to [RD19]:

Precise ephemeris, and so the precise position of satellites;
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


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Constellation almanac;
Reference position (of the terminal) and reference time;
Ionospheric corrections;
Acquisition parameters (estimated Doppler shift).
A positioning server at the network level is in charge of generating assistance data (aiding
information), but normally it can also compute the user position on the basis of the
observables sent by the user to the server. It is, in fact, possible that the positioning server
can be connected to augmentation systems, such as local differential correction networks, as
well as wide area GNSS augmentation systems (e.g. EGNOS/EDAS), providing increased
accuracy. The communication between the terminal and the positioning server can be set up
using two approaches:


Control-plane, in which assistance data are sent via pre-defined cellular network signal
structures (e.g. 3G, 4G);
User-plane, in which assistance data are sent via a general TCP/IP data connection,
thus not requiring any wireless standard specific messages.
Solutions for the user-plane A-GNSS approaches have been developed and standardized by
the Open Mobile Alliance [RD20]. Note that the user-plane approach allows, in principle, the
creation of a local assistance infrastructure, using, for example, a wireless ad hoc/sensor
network having a positioning server available at the network management level. This
approach can be employed in peer-to-peer relative positioning, where sensors are distributing
external navigation augmentations among them.
In the last decade, cellular network standard protocols have allocated resources to carry
GNSS assistance data to GNSS-enabled mobile devices, in order to implement AGPS/AGNSS
services in both Global System for Mobile Communications (GSM) and Universal Mobile
Telecommunications System (UMTS) networks. 3GPP boosted location services in Long-Term
Evolution (LTE) release 9, frozen in December 2009 [RD21].
Due to their wide adoption in commercial devices, SBAS and AGNSS can be considered as
consolidated technologies in the state-of-the-art for mass-market applications, thus
they will not be further discussed in this document.
3.3. HYBRIDIZATION WITH EXTERNAL SOURCES
Broadly speaking, the information provided by the aiding and augmentation systems
discussed above is intended to improve the quality of the received signal (thanks to the signal
corrections given by the augmentation systems) or to facilitate the receivers’ signal
processing (thanks to the assistance messages provided by the aiding systems); however,
this additional information intrinsically refers to the GNSS signal-in-space, but does not
contain any direct clue related to the position or motion of the platform (e.g., the vehicle) on
which the receiver is mounted.
On the other hand, it is common to extract information about the (relative) motion of a
vehicle from several on-board sensors, either motion sensors (e.g., steering encoder,
odometer, wheel velocity encoders) or inertial sensors (e.g., accelerometers and gyroscopes).
In this section we briefly review the currently employed approaches to integrate the
information provided by such external on-board sensors within the GNSS positioning
functionality of the receiver. The use of map matching algorithms is also cited as a common
method to improve the perception of accuracy of personal navigation devices.
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A comprehensive treatment of the on-board technologies used for GNSS-based ground
vehicles guidance and control is given in [RD07].
3.3.1. INTEGRATION WITH INERTIAL SENSORS
An Inertial Measurement Unit (IMU) is able to provide high data rate of acceleration and
rotation estimates of the inertial sensor. Since the sensor is rigidly mounted on-board of the
vehicle in a known and calibrated position with respect to the vehicle centre of mass, the
inertial information can be used, as an example, for dead-reckoning.
An Inertial Navigation System (INS) output is computed using the data provided by the
inertial sensors (mechanization). The INS provides a high-rate navigation solution (typically
100 Hz), which is limited by the choice of the computational approach and equipment. Since
such position solution only depends on the measurements of the IMU, it is intrinsically
immune to external radio interferences.
Unfortunately, measurements taken from inertial sensors exhibit a noise relatively low from
second to second, but this tends to drift over time due to the inherent error of the inertial
sensors. Since the mechanization process computes body’s displacements and velocities by
integrating over time the inertial measurements (namely, acceleration and rotation angles),
any measurement error is integrated over time, and it is reflected in an unbounded position
and velocity error in the long-term.
It is common to categorize IMUs on the basis of their accuracy and price. Table 3.1 illustrates
the characteristics of different grades of IMUs, giving the order of magnitude of the biases
they are affected, their price segment and some typical applications in which they are
employed [RD22]. The segment of interest for this study is clearly the automotive sector (last
column in Table 3.1).
As evident from the table, the positional error (drift) of an IMU for automotive applications,
with a cost up to 1000 USD, is high (more than 70 km/h). Leveraging on the progress in the
field of sensors technology, low cost Micro Electro-Mechanical Systems (MEMS) are now the
reference for applications where requirements in term of package dimensions and costs are
stringent. For example, MEMS are now integrated in smart phones and embedded systems for
mobile platforms and are available for less than 100 USD.
GRADE
FEATURE
Navigation
Tactical
Automotive
POSITIONAL ERROR
1-4 km/h
20-40 km/h
20-40 m/s
(72-144 km/h)
ACCELEROMETER BIAS [µg]
50-500
500-1000
> 1200
GYRO BIAS [°/h]
0.005-0.01
0.1-10
> 100
PRICE [kUSD]
50-200
10-50
<1
APPLICATIONS
General navigation
applications,
high
accuracy
georeferencing
Short
time
applications
Short
time
applications
Table 3.1: IMUs classification.
Opposite to inertial measurements, GNSS measurements are relatively noisy from second to
second, but their biases are bounded, so the GNSS positioning does not exhibit long-term
drifts. Thus, GNSS provide position and velocity estimation with bounded estimation errors.
Nonetheless, this information has a low rate (typically between 1 Hz or 10 Hz) and is
susceptible to jamming, blockage, interference and any impairment on the RF signal.
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Basically, GNSS and inertial measurements are complementary for two reasons: the
characteristics of their errors are different and these are measurements of different
quantities. In the last decades the fusion between these two systems has been implemented
in many navigation applications because it provides better performance than their standalone
operation, which is a consequence of its complementary nature.
The GNSS/INS fusion seeks to take advantage of the synergy as described below:
1. the INS provides navigation information when the GNSS signal is not available;
2. GNSS can be used to correct the INS estimates by an integrated navigation filter that
combines inertial system and GNSS measurements;
3. the GNSS/INS integration exceeds the accuracy of the GNSS alone. This is more
apparent in scenarios where the GNSS is affected by multipath and non-line-of-sight
propagation;
4. the hybridization with an INS provides a navigation solution at a rate much higher
than typical GNSS receivers.
Four different categories of integration approaches are usually adopted:




un-coupled;
Loosely Coupled (LC);
Tightly Coupled (TC);
Ultra-Tightly Coupled.
The first method (un-coupled) is the simplest integration of GNSS and INS. The two systems
operate independently, but as soon as a GNSS position and velocity measurement is
available, the INS mechanization is reset, so as to get rid of the accumulated biases [RD23].
This method does not provide any performance enhancement with respect to the stand-alone
GNSS accuracy; nonetheless it improves positioning availability in case of GNSS outage and
increase the rate at which a fresh position and velocity estimate is generated.
The second approach (LC) can be considered the state of the art in automotive applications.
The position and velocity measurements from both systems are integrated in a Kalman filter
that models INS error dynamics and creates a third blended navigation solution [RD24].
Today all the Commercial-On-The-Shelf (COTS) low-cost INSs that already provide a hybrid
position by fusing GNSS and IMU measurements (e.g., XSense, MicroStrain) follow a looselycoupled approach.
The TC technique uses estimates of pseudoranges and Doppler frequency extracted from the
GNSS receiver and predicted on the basis of the inertial estimates as measurements within an
blending Kalman filter. The main advantage of a TC system is in its ability to use even a
single GNSS range measurement to aid the position computation [RD24]. Therefore, the TC
makes the navigation more robust and is particularly suitable in scenarios where the
reception of satellites signals is critical, like urban canyons. The TC approach is not yet
state of the art, but the interest for this type of integration is growing in road
applications where it is possible to include MEMS in the navigation system.
In the Ultra-Tightly Coupled approach, integration occurs at the GNSS tracking loops which
are controlled by the blended navigation filter: the position, velocity and time outputs from
the navigation processor are projected onto the satellites-to-receiver line-of-sight directions
and are used to control the tracking loops of code and carrier for each satellite channel inside
the core receiver processing unit [RD25]. The Ultra-Tightly Coupled technique further
increases the robustness of the TC integration and is suitable for high dynamic applications.
The data fusion in computed at the level of the receiver’s core tracking loops, therefore it
requires a specific signal processing architecture which differs from those implemented in
commercial receivers. The GNSS receiver in this case is no longer an independent navigator
since its operation is also partly dependent on INS information. The potential benefits of such
an integration are achieved at the expense of a significant increase in design complexity,
computational load, and tight time synchronization [RD26].
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Furthermore, the stability of the solution is highly dependent on the quality of the IMU
sensors so that the effectiveness of the approach is questionable for INS of grade lower than
tactical. For these reasons, the Ultra-Tightly Coupled approach is well beyond the state of the
art for road applications.
3.3.2. DEAD RECKONING
To counteract navigation solution degradation in situations with poor satellite constellation
geometry, shadowing, and multipath propagation of satellite signals, advanced in-car
navigation systems commonly use complementary navigation methods, relying upon
information from motion sensors such as accelerometers, gyroscopes, and odometers.
The process of transforming the measurements from the vehicle-mounted motion sensor into
an estimate of the vehicles position and attitude is generally referred to as Dead Reckoning
(DR). DR is also called “inertial navigation” if it only involves inertial sensors [RD27].
In Table 3.2 below, the most commonly used sensors, together with the information that they
provide, are summarized.
Sensor
Measurement
Steering encoder
Front wheel direction
Odometer
Travelled distance
Velocity encoders
Wheel velocities
Electronic compass
Heading relative magnetic North
Accelerometer (inertial)
Acceleration
Gyroscope (inertial)
Angular rotation
Table 3.2: Sensors commonly used as complement to GNSS receivers for enhancement of in-car
navigation systems (from [RD27]).
All the measurements in the table only contain information on the relative movement of the
vehicle and no absolute positioning or attitude information. The translation of these sensor
measurements into position and attitude estimates will therefore be of an integrative nature,
requiring that the initial state of the vehicle is known. Moreover, the information provided by
the vehicle-mounted sensor is represented in the vehicle coordinate system; therefore, before
the sensor measurements are processed into a position, velocity, and attitude estimate, they
must be transformed into a coordinate system where they are more easily interpreted. Since
GNSS naturally gives an absolute position in an Earth-Centred-Earth-Fixed (ECEF) reference
frame [RD99], it naturally complements DR/inertial navigation with the initial state it needs,
when available.
The process of DR (and inertial navigation) can briefly be described as follows [RD27]:
1. The gyroscope, compass, or differences in wheel speed measurements are used to
determine the attitude (3-D) or heading (2-D) of the vehicle.
2. Attitude (or heading) information is then used to project the in-vehicle coordinates
measured acceleration, velocity, or travelled distance onto the coordinate axes of the
preferred navigation coordinates system, e.g., the ECEF coordinate frame.
3. Travelled distance, velocity, or acceleration is then integrated over time to obtain
position and velocity estimates in the navigation coordinate frame.
To give an example, Siemens’ car navigation system uses a gyroscope and odometer to
perform dead reckoning (DR). The trajectory estimated from DR is then projected onto the
digital map. If the estimated position is between several roads, several projections are done,
and the likelihood of each projection is estimated based on the information from the GPS
receiver and the development of the trajectory over time [RD28] [RD27]. Pioneer is another
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manufacturer that has presented advanced consumer in-car navigation systems ([RD29],
[RD27]), based on both DR and local attributes stored in a digital map database.
To summarize, the properties of DR systems (including inertial systems) are complementary
to those of the GNSS. These properties are given as follows [RD27].
1. They are self-contained, i.e., they do not rely on any external source of information
that can be disturbed or blocked.
2. The update rate and dynamic bandwidth of the systems are mainly set by the system’s
computational power and the bandwidth of the sensors.
3. The integrative nature of the systems results in a position error that grows without
bound as a function of the operation time or travelled distance.
Contrary to these properties, the GNSS receivers give position and velocity estimates with a
bounded error, but at a relatively low rate, and depend on information from an external
source that may be disturbed. The complementary features of the two types of systems make
their integration favourable and, if properly done, results in navigation systems with higher
update rates, accuracy and capability to provide a more continuous navigation solution under
various conditions and environments.
Odometers and velocity and steering encoders have proven to be very reliable DR sensors.
For movements in a planar environment, they can provide reliable navigation solutions during
several minutes of GNSS outages. However, in environments that significantly violate the
assumption of a planar environment, accuracy is drastically reduced [RD27] [RD30].
3.3.3. MAP MATCHING
Nowadays a digital map is available to work with GNSS receivers in most applications related
to the road domain, and therefore Map Matching (MM) can be seen in a broad sense as
another hybridization method of the GNSS positioning with external information sources.
Indeed, a map-matching algorithm could be seen as a component to improve the accuracy
performance of a navigation system.
MM is aimed at determining the vehicle location on a road given a map and a series of
positioning results. During last two decades quite many research papers on MM, from simple
search techniques to complicated mathematical methods, appeared in the literature
[RD31][RD32]. MM algorithms integrate positioning data with spatial road network data
(roadway centrelines) to identify the correct link on which a vehicle is travelling and to
determine the location of a vehicle on that link. In short, MM algorithms are used to
determine the location of a vehicle on a road segment.
Approaches for MM algorithms can be categorized into four groups: geometric, topological,
probabilistic, and other advanced techniques [RD31][RD32].
In MM the geometric and topological algorithms have the basic problem of choosing correct
links when facing a junction and with inaccurate GNSS heading information due to the low
speed. It is quite common that the vehicle has to decelerate and stop in front of the traffic
lights at a street junction. During this waiting time, MM algorithms may give wrong link
information due to the IMU drift errors and the inaccurate GNSS heading.
So far the information flow has been “unidirectional”, from the position information provided
by the positioning engine (i.e., the GNSS receiver, possibly assisted by other information
sources) to the MM algorithm, which associates the estimated vehicle position to the likeliest
point (under a certain criterion) onto the map. This approach has the potential of increasing
the final positioning accuracy perceived by the user and is common to any personal
navigation device in the market.
The potentialities of a “bidirectional” information flow between the positioning engine and an
enhanced digital map are an open topic of research nowadays and will be discussed in Section
5.7 Advanced digital map matching.
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4. POSITIONING PERFORMANCE FEATURES IN ROAD DOMAIN
The expansion of terrestrial applications including LBS and transportation means such as road
or train has fostered the design of complex location systems to comply with the needs of
these applications. In the frame of the service provision ensured by the application towards a
user or an external entity, these location systems are in charge of providing a consolidated
information based on the position of one or more mobile platforms.
The complexity of the information reported depends on the type of service targeted. It can
range from a simple position reporting in the case of a low end asset management, to the
provision of a reliable information (e.g. authenticated and with a mastered uncertainty) on
the mobile’s trajectory for liability critical services such as road charging or Intelligent
Transport System (ITS).
This wide spectrum of required technical features calls for a new and broader concept at
location system level taking into account hybrid solutions in which the use of GNSS
technologies is complemented with other sensors to improve the robustness and the
performance of the solution.
4.1. MAIN GNSS-BASED APPLICATIONS IN ROAD DOMAIN AND THEIR NEEDS
The scope of this section is to identify the main GNSS-based applications in the road domain relying on both already performed analysis and literature review - and to gather them in a set
of few classes, each of which referring to some key functions (Section 4.1.1). Then, for each
identified class of applications, the needs for improved positioning performances (e.g.
robustness, increased accuracy, etc.) are discussed in Section 4.1.2.
4.1.1. ROAD APPLICATIONS SURVEY
The road sector is a major potential market for GNSS applications and satellite navigation
receivers are now commonly installed in new cars as a key tool for providing new services to
people on the move. [RD33] states that the number of embedded devices and in-vehicle units
is growing, replacing traditional PNDs, and smartphones are more and more used for road
navigation purposes. In this scenario, there is also an increasing regulatory pressure for
emergency location sharing (e.g., eCall1) and safety-related applications.
At least six major classes of road applications enabled by satellite navigation can be
recognised [RD38]:
-
Road Navigation: route guidance using satellite navigation (“car navigation”) is already
a well-established product offered both by car manufacturers and standalone navigation
devices. The majority of these systems are based on satellite navigation systems that can
be integrated with onboard sensors (i.e., odometer and gyros) to compute optimal routes
in real-time also relying on the combination with electronic maps. Advanced Driver
Assistance Systems (ADAS) are other examples of system enabling Road Navigation
applications, being developed to increase the driver’s comfort and safety. ADAS can range
from the basic cruise control to the radar-based adaptive cruise control (ACC), from the
lane-keeping system to the collision-warning systems;
-
Tolling: road and urban tolling based on GNSS implies that the position and trajectory of
a vehicle is determined using GNSS in order to decide if the vehicle must be charged or
not and to compute the charging value. Representative examples of such “location based
1
According to a new Regulation recently approved by the European Parliament [RD34], all new types of
cars and vans will need to have an eCall system from 31 March 2018 onward. Such system is intended
to send and emergency call in case of accident, providing the vehicle identity and its GNSS-based
location, aiming to shorten the response time for emergency services.
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charging” applications are road user charging (RUC) and on-street parking billing. But also
pay-per-use insurance (PPUI) – enabling pricing policies based on timing, location and
driving behaviour – can be included in this class;
-
Emergency Services: the use of GNSS for emergency services and incident
management can make the response to emergency situations much faster and efficient
saving lives. The precise location of vehicle (as for the eCall system) can be sent to rescue
authorities and can use the emergency and rescue vehicle fleet management system to
assign the most adequate vehicle to respond to the incident;
-
Traffic Management: the monitoring and management of traffic fluidity will be
significantly facilitated when a great number of cars are equipped with satellite navigation
receivers and guidance systems (e.g., real-time road and traffic info services aiming, for
instance, to anticipate a traffic jam and suggest approaching vehicles to choose different
route);
-
Fleet Management and Vehicle Tracking: GNSS-based fleet management systems are
used to locate vehicles (e.g. trucks, buses, police cars, taxis) in order to optimize resource
management, reduce travel time, increase security and reduce fuel consumption. As a
consequence of this significant growing new ITS services are expected to be deployed in
the coming years, taking the use of GNSS far beyond in-vehicle navigation. A quite
specific Vehicle Tracking application driven by regulation is the Digital Tachograph (DT) , a
recorder for professional drivers’ activities aiming to enforce rules on driving times and
rest periods, then guaranteeing far competition and road safety [RD35]. The new EU
regulation introduces the use of GNSS positioning in the future DTs, also fostering –
according to [RD33] – the use of satellite-based positioning authentication to guarantee
the origin and the trustability of the DTs records.
Even if the expected output of a GNSS-based positioning system2 is basically the same for all
the enabled applications (i.e., the position, the velocity and the time of the road user car),
each specific application may ask for specific positioning performances according to the
service provided (or improved performance in supporting future evolution). For instance, a
reliable and accurate localization is strictly needed for eCall, while an authenticated Position
Velocity and Time (PVT) may effectively support the tracking of hazardous material
transportation.
Within the framework of one of the most significant running standardization process [RD37],
possible GNSS-enabled applications in the road domain have been gathered in a set of
different classes (see Table 4.1 below), each referring to some key functions in terms of
positioning (e.g. reliability, accuracy).
2
According to [RD39], a positioning system is the “set of hardware and software components, which
can be in different locations, but interconnected, which contribute to estimating the position, speed and
associated timestamp of a mobile”. The terminal component of a positioning system (i.e. the unit
carried by the vehicle, typically a GNSS receiver which may also be hybridised or assisted) is referred as
“positioning terminal”.
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Class
Examples of applications3
Key functions
Location based charging
Road user charging
- Reliability of the detection of virtual
gates crossing
- Billing service unavailability
On street parking billing
PAYD charging
Pay per use insurance
- Representativity of the computed
distance
- Representativity of the reported
trajectory
Cooperative basic
geo-localization reporting
Transport on demand
- Reported position accuracy
- Location service availability
Road and traffic data collection
(V2V – V2I technologies)
Non-cooperative basic
geo-localization reporting
Recovery after theft
Reliable geo-localization
eCall
- Reported position accuracy
- Location service availability
- Confidence level associated to the
reported parameter
Reliable vehicle movement
sensing
Legal speed enforcement
- Movement caption accuracy
- Confidence level associated to the
reported parameter
Fleet management
Eco-driving and carbon
emission foot-printing
- Reported position accuracy
- Location service availability
- Service reliability, including
spoofing attempts detection
Table 4.1: List of possible application classes (from [RD37]).
At the end, considering the outcomes above but having in mind the ultimate scope of this
survey (i.e., gather the GNSS-based applications in the road domain in a set of few classes,
each of which referring to some key function) and also taking into account the GNSS market
of today [RD33], for the purposes of this document the following classification is proposed:
3
Another lists of possible applications can be found in a recent EC study [RD36] dealing with the
certification of EGNSS-based road transport applications: digital tachograph, eCall, in-vehicle signage,
intelligent speed adaptation, limited traffic area monitoring and control (urban), location-based billing,
PAYD insurance, regulated fleet management and control (urban), remote vehicle immobilisation, road
user charging, wrong way (ghost) driver).
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Safety-Critical Applications
(SCA)
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Applications in which the life of the vehicle’s driver
directly depends on the performance of the GNSSbased in-vehicle positioning system (e.g. accuracy,
availability, integrity).
All the autonomous driving applications are SCA, but
also some advanced Road Navigation aid (e.g., ADAS),
the Hazardous Material Tracking, and some Emergency
Services.
Payment-Critical Applications
(PCA)
Applications in which the performance of the GNSSbased in-vehicle positioning system may have
economic implications, being the usage of a vehicle (in
terms of its position over the time) subject to a
payment.
All location-based charging applications are PCA by
definition, e.g. RUC, PAYD, and PPUI.
Regulatory-Critical Applications
(RCA)
Applications enforced by transport policies induced by
national or international legislations, where the use of
the GNSS-based positioning is fostered to guarantee
the intended requirements.
Even if potentially eligible as a SCA (being an
“emergency services”), the pan-European eCall is
actually a regulated applications. The same is for the
Digital Tachograph.
4.1.2. NEEDS FOR IMPROVING POSITIONING PERFORMANCE
Starting from the survey performed in the previous section and particularly considering the
proposed classification, this section deals with the common needs for improved positioning
performance for clusters of GNSS-based road applications.
SCA
Road Navigation - enhanced
Autonomous driving
Autonomous vehicles are enabled by the combination of different technologies and
sensors, allowing the in-vehicle system to identify the proper actions. GNSS plays a
key role in supporting autonomous vehicles by providing relevant inputs for integrated
navigation, such as accurate position and speed of the vehicle. Because the critical
implications of any loss of GNSS (even considering the integration of other sensors),
the position and speed estimation shall be highly available. Moreover, as a common
need for all the SCA, the position (and speed, in this case) shall come with a
statement/indicator about the statistical confidence associated to the estimation
process.
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Road Navigation – enhanced
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ADAS (e.g. “safe” speed advice)
In general terms, ADAS are systems intended to help the vehicle’s drivers in
ensuring a safety and better driving. For instance, ADAS may automate lighting,
provide adaptive cruise control, automate braking, alert driver to other cars or
dangers, keep the driver in the correct lane, or show what is in blind spots. For the
purpose of our analysis, only the “safe” speed advice feature is considered here as
an example.
With the aim to inform the driver when his/her speed appears to be too high for the
road and the traffic or weather conditions, at first the estimated position shall be
accurately map-matched on the proper road segment (to which all relevant
information are associated). A mismatching could result in an erroneous “safe” speed
suggestion, possibly leading to accident. In addition, the speed of the vehicle shall be
accurately estimated as well. Moreover, as a common need for all the SCA, the
position (and speed, in this case) shall come with a statement/indicator about the
statistical confidence associated to the estimation process.
Fleet Management – enhanced
Hazardous Material Tracking
The position and the speed of the truck transporting the hazardous material shall be
quite continuously available and quite accurate enough to follow the transport along
the road network (possibly map-matching each position on a specific road segment).
In this scenario, considering the peculiarity of the transported material, the
robustness against any malevolent spoofing attack shall be ensured.
PCA
Tolling
RUC, on-street parking billing
(“location-based charging” in general)
Tolling applications refer to the location-based charging applications, where the virtual
gates crossing of a vehicle is detected or the time spent inside a certain perimeter by a
vehicle is estimated. Moreover, considering that the commercial transactions are
basically based on the position of the vehicle, the robustness against any
malevolent spoofing attack shall be ensured: in fact, self-spoofing attacks can be
put in place to provide false position data in order to pay less.
Pay-per-use services
PAYD, PPUI
Pay-As-You-Drive (PAYD) and Pay-Per-Use-Insurance (PPUI) are the typical
applications which charge a user on the basis of the time spent driving across certain
extended areas (for example, an insurance fee per hour could be higher if the car is
driven across a city area than along rural roads).
For this reason, a correct assessment of the position authenticity and the
robustness to interference are fundamental.
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RCA
Emergency Services
eCall
Whatever the trigger for the emergency call (i.e., either automatic by vehicle’s sensors
in case of an accident or manual by the driver or witnesses in nearby cars), the caller’s
position provided to the emergency responder shall be available (as output of the
positioning system in the vehicle) and accurate, possibly map-matched on the road.
Moreover, the position shall come with a statement/indicator about the statistical
confidence associated to the position estimation process.
Road Navigation - enhanced
Navigation for emergency vehicles4
Even if not a RCA, the navigation aid for the professional vehicles involved in
emergency services (triggered by an eCall) is treated here because of its inherent
relationship with eCall.
Once the accurate position of the vehicle involved in an accident is available (see
Emergency Services above), the emergency services shall be properly guided towards
the location of the accident. Here “properly” has to be intended that the positioning
system in the emergency vehicle shall be prompt to accurately estimate its position
(asking for a reduced time-to-first-fix) but also available along the route.
Moreover, the position shall come with a statement/indicator about the statistical
confidence associated to the estimation process.
Vehicle Tracking
Digital Tachograph
For the purposes of DT, the start and the end position of the any work session shall be
automatically recorded (vehicle movement detection is needed, in redundancy with the
vehicle's sensors), as well as the time even if without stressed accuracy requirements.
New regulations are being issued in Europe in order to increase the reliability and the
trustworthiness of the recorder data by mandating the inclusion of GNSS capabilities in
future DT devices.
As such, the demand for the position authenticity, robustness to interference,
and integrity of the estimated position is clearly posed, as well as for availability,
whereas the requirement in terms of continuity seems less stringent with respect to
SCA applications.
4.2. POSITIONING PERFORMANCE FEATURES
According to [RD39], a “performance feature” is a given characteristic used to qualify and
quantify the service provided by a generic system, e.g. horizontal accuracy for a positioning
system. Moreover, the definition of the means of measuring a given performance feature of a
system represents the “performance metric”. An example of metric for the performance
feature “accuracy” can be the median value of an error sample acquired during a given test
following a given protocol.
Table 4.2 (merging information from [RD38], [RD39], [RD40]) lists a set of possible
“performance features”, providing a formal and not ambiguous definition for each feature
together with a draft consideration on the relevance of each feature for the positioning needs
above.
4
Even if not a RCA, the navigation aid for the professional vehicles involved in emergency services
(triggered by an eCall) is treated here because of its inherent relationship with eCall.
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Horizontal position accuracy
Statistical measure of the horizontal position (or velocity) error (e.g. 95th percentiles of the
cumulative error distribution), being this error the difference between the true horizontal
position and the position (or velocity) estimated by a positioning system at a given time.
The requirements for this feature can range from relaxed constraints for personal navigation
applications, to more stringent ones for LCA such as road user charging and tracking of dangerous
goods.
Vertical position accuracy
Statistical measure of the vertical position error (e.g. 95 th percentiles of the cumulative
error distribution), being this error the difference between the true vertical position and the
position estimated by a positioning system at a given time.
This feature applies when vertical guidance is required, for instance to allow proper positioning in
case of parkade (multi-levels parking) or overlapping road segments, especially in urban
environments.
GNSS time accuracy
Statistical measure of the GNSS time error (e.g. 95th percentiles of the cumulative error
distribution), being this error the difference between the true GNSS time (as implemented
in the GNSS system timing facility) and the time returned by the positioning system based
on the PVT solution.
Generally, this feature is of interest for applications requiring synchronisation of assets distributed
across wide geographical areas, where GNSS time is used as a reference. Focusing on the road
sector, GNSS time accuracy applies for example in case on VANET applications (involving a very large
number of distributed nodes) that in future might require the use of synchronous Medium Access
Control (MAC) in order to overcome the known scalability issue of the decentralized and
asynchronous Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) method.
Time-to-first-fix (TTFF)
Time taken by the positioning system to report a PVT solution (fix) starting either from the
reception of a specific “start” request, or from another triggering event that switches the
positioning system on.
This feature is of particular interest for the navigation support (route guidance) of emergency
vehicles, provided that the positioning system in the emergency vehicle has to be prompt to
accurately estimate its position.
Position authenticity
Authenticity gives a level of assurance that the data provided by a positioning system has
been derived from real signals.
RF spoofing may affect the positioning system resulting in false position data as output of the system
itself.
Robustness to interference
Ability of the positioning system to operate under interference conditions and to maintain
the applicable positioning service level requirements.
Location Systems might be required to operate in constrained RF environments, in particular in the
GNSS frequency bands. Note that interference can be either unintentional or deliberate (e.g.
jamming)
Position integrity
General performance feature referring to the trust a user can have in the value of a given
PVT provided by a positioning system.
It is relevant to SCA and LCA (e.g. critical navigation, billing) where integrity is important. It is
expressed through the computation of a protection level associated to a predetermined integrity risk,
as a function of the type end-user application.
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GNSS sensitivity
Minimum GNSS signal strength at the antenna, detectable by the receiver (dBW or dBm).
The GNSS sensitivity is a relevant feature in all the applications involving possible urban and light
indoor scenarios (especially eCall and emergency services).
Availability
The availability of a navigation system is the percentage of time that the services of the
system are usable by the users for navigation purposes. Availability is an indication of the
ability of the system to provide usable service within the specified coverage area.
The availability is one of the most important performance features in supporting any safety-critical
application, e.g. emergency services.
Continuity
Continuity is defined as the operation given that the service level requirements are
provided at the start of the capability of a system to provide a positioning service fulfilling a
set of applicable service level requirements, throughout the intended operation.
Table 4.2: Definition of the positioning performance features
According to the analysis of the positioning needs for the identified main classes of road
applications (Section 4.1.2), each performance feature above plays a different role in
supporting the expected needs. A statement about the relevance – in terms of a simple but
effective 3-level scale, i.e. Low-Medium-High – of each performance feature for the identified
needs can be drafted. Table 4.3 below summarizes this analysis.
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Robustness to
interference 9
GNSS
sensitivity
Availability
Position
integrity 11
Continuity 11
H
M
L
M
L
M
H
H
Road Navigation – enhanced (ADAS)
M/H
M
M
L
M
L
M
H
H
Fleet Management – enhanced (HMT)
L/M
M
M
H
H
M
H
H
L/M
PCA
L
M
H
H
M
H
H
L
RCA
Autonomous driving
Location-based charging (RUC)
L/M
12
7
6
11
Position
authenticity
SCA
10
TTFF
H
8
Time
accuracy
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Position
accuracy 5
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L
L
L
H
H
M
M
H
L/M
Emergency Services – eCall
M
L
M/H
L
H
H
H
M
L
Road Navigation – supporting emergency
M
L
M/H
L
H
H
H
M
L
Vehicle Tracking – DT
L
L
L
H
H
L/M
M
M/H
L
Table 4.3: Correlation between the application needs, gathered in terms of criticality, and the performance features.
5
Roughly speaking, “L” means > 10 meters, while “H” means < 2 meters for the position accuracy feature.
6
The time accuracy feature can be approximately mapped as follows: “L” means > 1 s, “M” means 1 ms ÷ 1 s, and “H” means < 1 µs.
7
The relevance of the Time To First Fix feature is broadly related to the need for hot/warm/cold start conditions. For this reason it is possible to set
“L” > 30 s, “M” < 30 s, and “H” < 1 s.
8
As clearly stated in the definitions in Table 4.2, the position authenticity is strictly related to the RF spoofing (that results in counterfeit position data).
9
“Robustness” has to be intended here as the capability to ensure a prompt detection of any interference (including jamming) on the GNSS bands.
10
Aiming to assess the relevance of GNSS sensitivity, the availability of on-board equipment other than GNSS receiver (e.g. radar, cameras and other
sensors used in an ADAS) is taken into account, being such redundancy enough to reduce the relevance of GNSS sensitivity. Typical values declared for
high-sensitivity receivers are on the order of -160 dBm or better, while medium-sensitivity are on the order of -148 dBm. Lower performances are typically
not declared.
11
12
Availability, position integrity, and continuity features are further discussed in D.3.2 [RD124].
The wide range of location based charging applications (e.g. from the RUC to on-street billing) turns into a quite wide range of positioning accuracy
needs. For instance, charging applications in urban scenarios are more demanding than along highways.
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4.3. TECHNOLOGICAL ENABLERS
Combination of GNSS receivers with vision sensors
Sect. 5.3.1

Tight integration with IMU
Sect. 5.3.2

Civilian GNSS signals authentication
Sect. 5.4.2

GNSS spoofing countermeasures
Sect. 5.4.3

Integration with SoO – DVB-T
Sect. 5.5.1
Integration with SoO – Cellular networks
Sect. 5.5.2
Integration with SoO – DSRC
Sect. 5.5.3

Precise Positioning – PPP
Sect. 5.6.1

Precise Positioning – RTK
Sect. 5.6.2

Advanced digital map matching
Sect. 5.7




Continuity

Position
integrity

Availability
Sect. 5.2
TTFF
Multi-frequency receivers
TECHNOLOGICAL ENABLERS
GNSS
sensitivity
Robustness
to
interference

Time
accuracy

Position
accuracy
PERFORMANCE FEATURES
Position
authenticity
Table 4.4 lists all the identified technological enablers that it is expected to effectively achieve the performance features above (Section
4.2). Each technological enabler is detailed in Section 5.



















Table 4.4: Correlation between the performance features and the identified technological enablers.



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5. TECHNOLOGIES SURVEY
This section provides an investigation of the main technological enablers identified in Section
4.3, considering potential advance beyond the state-of-the-art. The scope is not to give a
detail insight of each technology (additional resources are provided in Section 7), but to focus
on the key aspects in supporting the expected positioning performance. Apart from a strict
technological perspective, other relevant viewpoints are taken into account when possible,
e.g. complexity, maturity, costs.
The goal is to make available a sort of evaluation of the potentiality of the identified
technologies.
5.1. EVALUATION CRITERIA
This section provides the criteria used in the following sections for the evaluation of the
various technologies identified in Section 4.3. A list of criteria is provided in the table below,
where a rank description (nature, range, etc.) is associated to each criterion.
Criteria
Description
Rank (Low, Medium,
or High)
It should assess the capability of
the specific technology to fulfil
the expected positioning
performance.
L (marginal) to
H (significant)
Technological readiness13
It should assess the maturity of
the technology at present.
L (concept) to
H (prototype)
Time-to-market
It should assess the potential of
the vehicle on-board equipment
and/or possible system
components to be promptly
implemented aiming to support
the technology.
L (very short) to
H (long)
Performance
Refer to the performance features in
sect. 4.2.
Maturity
Cost
Estimated cost in 2015 (current)
Estimated cost in 2025
13
It should roughly assess (without
numbers) the estimated impact
of the technology on the cost of
the vehicle on-board equipment
and/or possible system
components (e.g. deployment of
access points, ad-hoc
communication networks, and
servers), both now and in 2025
respectively.
L (marginal) to
H (significant)
L (marginal) to
H (significant)
The technological readiness criteria is inspired to the Technological Readiness Levels (TRL) as
listed in the Annex G of the Horizon 2020 Work Programme 2014–2015 [RD41]. The following
qualitative mapping has been considered:

TRL 1-3 → Low (L)

TRL 4-6 → Medium (M)

TRL 7-9 → High (H)
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Challenges
Technological challenges
Framework conditions
and standards)
(i.e.
regulation
It should roughly assess possible
technological challenges (e.g.
GNSS signals not available in
indoor scenarios), any
barriers/obstacles and any
framework conditions (such as
the lack of regulations and
standards).
L (marginal) to
H (significant)
L (marginal) to
H (significant)
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5.2. MULTIFREQUENCY RECEIVERS
Today, mass market receivers are able to process GNSS signals from different constellations,
over a single frequency band. Performances of a quad-constellation mass market receiver are
reported in [RD10], while examples of products can be found in [RD11]. Generally, the mass
market class refers to receivers sold to the consumers, produced in high volume and with
considerable price pressure. These are thought for mobile phones and tablets as well as for
in-car systems and Personal Navigation Devices (PND).
Multi-frequency receivers for mass-market applications are not the state of the art yet. With
multi-frequency receivers, we intend receivers that, in addition to the L-band from 1560 to
1610 MHz, operate with other navigation signals broadcast in the range from 1170 to 1290
MHz. For instance, GPS L2C and L5 signals, GLONASS L2 and L3 signals and Galileo E5 and
E6 signals.
From a technical point of view, the main reason that would induce GNSS engineers to use
multiple frequencies also in mass-market receivers is to allow for an autonomous
measurement compensation of the ionospheric errors. Over the last years, the importance of
adding this capability has been reduced with iono corrections from EGNOS or through
terrestrial systems like EDAS, even if this last requires the availability of a communication
link. Although in most of road applications working in open sky the need of a dual frequency
receiver is not felt stringent (i.e.: the positioning accuracy of EGNOS-enabled receiver is < 2
meters), the use of multiple signals is the first step toward enabling enable the resolution of
the carrier ambiguity and in turn would ease methods for precise positioning. As anticipated
by [RD107], this fact is already important for the so called “light professional” market, where
the price pressure is much less extreme and users actually wants the benefits of multifrequency. The volumes of this market are tiny compared with consumer markets. Examples
of light professional applications are the transportation and tracking of dangerous goods, or
the precise positioning of special fleet of vehicles used for road maintenance. These are
applications that will also benefits of the Galileo commercial service (i.e.: authenticated signal
embedding PPP data) and will likely use dual frequency receivers able to process signal on the
E1 and E6 bands [RD69].
The use of an additional frequency in mass-market receivers is not complicated from a
technical point of view, but it comes with a cost, which seems currently the only barrier
limiting this technology. Despite what one could believe, the major part of the cost is
associated to the development process rather than the extra silicon and components needed
for the additional signals. Experts agree that a production of many millions of pieces can
amortize such cost.
At the time of writing, there is a debate on the use of multi frequency receivers for the next
generation of mass market applications. Some think that the market will take the benefits
provided by advances in power consumption and will remain single frequency, but multi
constellations. Others believe that soon mass market receivers will be able to process the
signals on E5/L5, in addition to those in E1/L1.
Observing what has been proposed in the scientific literature over the past two years, it
seems that chip manufacturers started the design of flexible front end architectures. These
allow for saving the development cost of new multi frequency chains, but preserve the
possibility to reuse the same chip for different frequencies. Such flexible architectures can be
used interchangeably for two frequencies. So, low cost applications will use just one (e.g.:
the GPS L1), whereas more demanding applications will employ two copy of the same RF
(e.g.: the first for the GPS L1, the second for the GPS L5) [RD107]. This approach leaves the
door open for dual-frequency solutions that in turn will allow full advantage of carrier phase
measurements. This is the basis for the application of PPP or RTK into the automotive market
for fields such as advanced driver-assistance systems.
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5.2.1. TECHNOLOGICAL ISSUES OF MULTIFREQUENCY RECEIVERS
Today low cost receivers for road applications are not multi frequency. The barrier is not due
to the level of maturity of the technology, but rather to the high costs associated to new
developments, that need very high volumes of productions to be amortized. Dual frequency
mass market receivers have been studied and prototyped with promising performance in the
last years [RD42], but:

it is not viable for chip manufactures to make silicon for low-volume combinations, nor
to divide the overall market over different chips.

chip manufactures believe that their single frequency mainstream chips should also
support the lower volume options, where the need of multi-frequency is more evident.

chip manufactures cannot impose silicon area or power consumption penalties on the
high-volume customers.

The design of flexible front ends, that could be reused for different frequency seems
an appropriated approach, otherwise the low volume of sales of a dedicated dual-band
radio would never repay its development costs.
5.2.2. PERSPECTIVES TOWARD THE ROAD SECTOR
Multifrequency GNSS receivers have the ability to strongly reduce the ionospheric error and
provide good performance in terms of position accuracy. However, multifrequency receivers
are not yet part of In-Vehicle System (IVS) used in mass-market applications, because their
adoption is strongly hindered by the high costs associated to new developments, that need
very high volumes of productions to be amortized.
However, some professional applications in the road domain require higher accuracy with
respect to single frequency receivers and motivate GNSS receiver manufacturers to offer
multi-frequency solutions [RD33], leveraging new constellation and signals.
Criteria
Score
Justification
Position accuracy
H
Increased position accuracy due to ability to
mitigate the ionospheric error.
Measurements performed over different
bands enable precise positioning (i.e.: PPP or
RTK).
Enabling the reception of the Galileo CS over
E6 band, then exploiting the high-accuracy
service (PPP data transmission).
Time accuracy
H
Increased time accuracy due to ability to
mitigate the ionospheric error.
Position authenticity
H
Enabling the reception of the Galileo CS over
E6 band, and then exploiting the signal
authentication service.
Robustness to interference
H
The frequency diversity is an intrinsic method
to mitigate interfering signals
Position integrity
M
In open sky conditions, dual frequency
receivers strongly reduce the ionospheric
error, which is the most dominant error
source. The improved accuracy decreases the
probability that the positioning error exceeds
the PL.
On the other hand, the algorithms combining
Performance
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measurements from different frequencies
amplify the error due to multipath, that is
likely in urban. In this case the performance
in terms of position integrity decrease.
Maturity
Technological readiness
H
Dual band RF receivers for GPS and Galileo
have been developed in recent R&D projects
(e.g.: [RD42]), showing the maturity of the
technology.
Time-to-market
M
Despite the high level of technological
readiness, the time to market of new
products cannot be considered short, mainly
due to the high costs associated to the
development of new chipsets
Estimated cost in 2015 (current)
H
The costs for the development of new multifrequency GNSS chipsets are still high and
represent the major barrier of this
technology
Estimated cost in 2025
M
It is expected that in ten years cost-related
constraints will be relaxed. Multi frequency
mass market receivers will be the state of
the art, at least for some professional
applications requiring medium-high
performance in terms of positioning accuracy
L
Architectures for dual frequency massmarket receivers have been developed,
validated and presented as prototype. The
main barrier for the adoption of
multifrequency receivers in road is not of
technological nature.
The design of flexible and reconfigurable RF
front end seems an appropriate approach to
match the requirements of professional users
and save the cost of new development.
Cost
Challenges
Technological challenges
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5.3. DEEP INTEGRATION WITH SENSORS
New LBSs in the transportation domain require complex location systems to comply with
specific user requirements and needs. Often the requirements in terms of accuracy, integrity
and data trustworthiness are stringent and call for a new and broader concept of location
systems, that takes into account hybrid solutions in which the use of GNSS is complemented
with sensors and other terrestrial technologies to improve performance.
GNSS receivers are today more and more combined with sensors installed on board of
vehicles. This section considers the integration with vision sensors and IMUs. The discussion
is around the “deep integration”, that is the data fusion between measurements from the
GNSS module and sensors at a low level, before the computation of the PVT.
5.3.1. COMBINATION OF GNSS RECEIVERS WITH VISION SENSORS
Many manufacturers have so far developed advanced driver assistance systems (ADAS)
based on vision sensors, which detect the presence of specific road features to provide the
driver with appropriate alert/information. For example, a lane departure warning (LDW)
system alerts the driver when the vehicle has driven outside the lane markings of the current
lane of travel. The feature used for vision-based LDW is the painted lane lines.
Most of the LDW systems in production now are solely based on video-cameras [RD07].
Cameras are a very popular type of hardware used today for driver assistance. Their
advantage is mainly in the limited cost of the hardware involved: digital cameras have been
in production for decades and the cost of these devices is relatively cheap. Also the
algorithms used for lane positioning using a camera are well established.
Lane positioning can also be accomplished using a light detection and ranging (LiDAR)
scanner. LiDAR measures the range to an object by pulsing a light wave at the object and
measuring the time between transmission and reception. The light wave for LiDAR
applications is a laser. LiDAR scanners combine the laser with a moving mirror that rotates
the laser’s beam. This can provide ranging information in multiple directions, both vertically
and horizontally. A LiDAR scanner with reflectivity measurements can be used to search for
lane markings. One advantage of using LiDAR is its robustness to varying lighting and
weather conditions; unlike a camera, LiDAR scanners work independent of surrounding
lighting conditions. However, the largest disadvantage of LiDAR scanners is their cost, which
has largely prevented so far implementation of LiDAR-based ADAS systems on civilian
vehicles.
Vision-based measurements, as well as radar measurements, used in most of the ADAS are
designed to provide information relative to the vehicle’s reference frame or the road-based
reference frame. However, recent research works propose the integration of such kind of
information in the “navigation filter”, in order to improve the accuracy of the final navigation
solution to the lane level [RD07].
Basically, the integration of visual information has been proposed at three stages inside the
navigation system of a manned vehicle:
1) As an additional measurement in input to the navigation filter stage, to enhance
accuracy of the estimated position (additional map info necessary);
2) At the inertial navigation system (INS) stage, to enhance the robustness of the INS in
GNSS-denied conditions;
3) At the pseudorange stage, in order to identify and exclude NLOS measurements before
their input to the navigation filter.
These approaches are reviewed in the following subparagraphs.
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5.3.1.1. Visual information as an additional measurement in input to the navigation
filter stage
To use visual information as an additional measurement in input to the navigation filter stage,
it is necessary to express this new information in a reference frame compatible with the other
existing measurements inside the navigation filter. Lane position measurements are given in
the road-based coordinate frame. The road-based coordinate frame can be approximated with
a waypoint-based map, in which the waypoints lie in the centre of the lane that is being
mapped; the distance between waypoints depends on the road geometry. Generally
speaking, in order to achieve lane-level positioning accuracy, the estimated global position
and velocity of the vehicle is reported, through an opportune roto-translation of the
coordinate frame, to the road-based reference frame, where the visual measurements of the
vehicle’s position with respect to the lane centre are available [RD07]. Thus, such
measurements can enter in the navigation filter as a measure of the distance of the current
vehicle position from the current map waypoint. If the map is sufficiently accurate, then a
lane-level positioning accuracy can be achieved. To apply this kind of approaches, the
position of the base waypoint of the map in the navigation coordinate frame must be known.
Also the rotation matrix from the navigation coordinate frame to the road coordinate frame
must be known, namely, the attitude of the road coordinate frame with respect to the
navigation coordinate frame must be known. All these elements must be saved in a map
database, which must be available to the navigation filter. Waypoint-based maps are
available through precise location survey.
5.3.1.2. Visual information at the INS stage
A different approach that exploits the information extracted from an on-board video camera
to enhance inertial navigation/dead reckoning in case of GNSS outage, can be found in
[RD43]. Here, a low cost video camera, such as one of those currently integrated in most
mobile devices, is used to calibrate a MEMS inertial sensor which suffer from significant bias,
drifts and noise in the absence of GPS measurements. If using the MEMS IMU alone, the
navigation solution will diverge to hundreds of meters within several minutes. In this context,
a camera can limit the IMU divergence by tracking the locations of optical features in
successive images. In this case no map features are involved. A conceptually similar
approach has been developed and implemented in a prototype GNSS/INSS/LiDAR software
receiver in [RD44]: when GNSS fails, the navigation uses LiDAR observations to keep
controlled the inertial sensor errors. Again, no map information is necessary. However,
limitations can be found in the stability of the algorithms that track optical features in very
challenging scenarios (for example in a crowded urban road, with cars parked along the
sidewalks and unpredictable changes of the street furniture) and in the cost of the LiDAR
equipment.
It must be noticed that such a family of approaches, based on complementing inertial
navigation with visual measurements in GNSS-denied environments [RD45], has been mainly
proposed so far for handheld indoor positioning [RD46][RD47][RD48].
5.3.1.3. Visual information to identify NLOS measurements
A third different way to exploit visual information to enhance positioning performance is
based on the idea that the navigation filter performance improves if GNSS pseudoranges
generated from non-line-of-sight (NLOS) satellites are excluded from the solution
computation: an omnidirectional video camera can easily detect the borderline between the
sky and the obstacles such as the buildings, thus enabling the exclusion of satellite signals
with NLOS directions of arrival [RD49][RD50]. A non-trivial problem is to robustly detect the
borderline between the sky and the obstacles from a coloured image, because detection using
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the colour camera is affected by cloud cover, illumination conditions, and weather conditions.
Infrared cameras are far less affected by such kind of problems [RD49][RD51].
5.3.1.4. Place recognition through visual information
A further family of approaches is exemplified by the work presented in [RD52]. It is a kind of
image-based “fingerprinting” [RD13], in which images taken at various locations in a
particular environment are recorded with their photo-taken positions and compiled to form a
database. Place-recognition (i.e., “fingerprinting”) is then achieved by associating the realtime image taken from unknown location in the environment with a geo-coded image from
the database that has the most similar appearance. Various algorithms are developed to
associate images with different perspectives by their content appearance. The major
drawbacks of such an approach are the necessity of compiling the geo-located image
database, its maintenance along the time, its availability to the positioning device and the
demanding computational cost associated to image processing. For these reasons this
technology seems unsuitable for the integration with GNSS, while it could have more chance
for specific indoor applications.
5.3.1.5. Technological Issues of Combination with vision sensors
The use of on-board visual sensors poses a number of issues, some at sensor level, others at
the integration level with GNSS.
At sensor level, currently the largest disadvantage of LiDAR-based systems is the cost of the
hardware. LiDAR is a relatively new technology with a still limited number of manufacturers.
Also, using LiDAR scanners to detect lane marking is a new and quite undeveloped science
[RD07].
On the other hand, video cameras are relatively cheap and their employment for lane
recognition is well established. However, disadvantages include vulnerability to lighting and
weather conditions; for example, at dawn and dusk, when the sun is low in the sky, a camera
may be blinded by the sunlight.
Independently from the sensor used, the extraction of visual information can be difficult in
urban environments where lane markings are in poor condition or visibility of lane markings
(and other optical features) are blocked by surrounding traffic.
When the integration of such sensors within the GNSS navigation filter is considered, other
issues arise.

First, a more complex signal processing in the navigation filter is necessary, in order to
deal with a type of measurements generated in a different coordinate reference frame.
Although this is not considered a technological barrier, the size and complexity, as well
as the availability, of the map database could be.

Second, the use of visual information as an aiding to the INS is in fact complementary
to the GNSS and seems to give little advantage when GNSS is present. The trade-off
between the cost/complexity of installing an additional visual source and the
cost/complexity of a more stable dead-reckoning (including IMU and non-inertial
motion sensors as sources) is still an open point.

Third, in general, the maturity of such integration algorithms seems not achieved yet
in non-controlled scenarios (i.e., in more complicated environments than those
expected for robotic automation, for example crowded urban roads), since the
“availability” and “continuity” of such visual information in different scenarios is not
clearly established yet.
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5.3.1.6. Perspectives toward the road sector
Today, most of vehicles have low cost visual sensors installed as part of the driver assistance
system. Even if in nominal conditions they can enhance the performance of GNSS receivers,
system integrators might be discouraged to invest in this type of technology, because low
cost visual sensors have performance still dependant to the external environment. Superior
grade sensors come with higher costs and their use is motivated only in autonomous driving
applications.
Criteria
Score
Justification
Position accuracy
H
The achievable accuracy in nominal conditions for
the two integration types “Visual information as an
additional measurement in input to the navigation
filter stage” and “Visual information at the INS
stage” is promised to be high (enabling lane-level
positioning)
Availability
L
Video-cameras are prone to errors dependent on
weather and lightning conditions. The likely high
variability of the optical features in non-controlled
scenarios may affect the availability and continuity
of visual information.
Continuity
L/M
The likely high variability of the optical features in
non-controlled scenarios may affect the availability
and continuity of visual information.
Nonetheless, when visual information is used for
aiding INS in the absence of GNSS (in particular in
tunnels), it can improve continuity and accuracy
Technological readiness
M (L)
M: Sensor technology is developed, although
improvements are expected
L: The maturity of the integration algorithms seems
not achieved yet in non-controlled scenarios (e.g.,
crowded urban roads) and needs to be assessed.
Time-to-market
M/H
Mentioned technological issues on the integration
between visual sensors and GNSS have to be
overcome before the integration with visual sensors
entering into the market.
Estimated cost in 2015 (current)
H
N.A
Estimated cost in 2025
H
N.A
M
LiDAR: high cost of the hardware
Video-cameras: vulnerability to lighting and
weather conditions
Difficult extraction of visual information in urban
environments (optical features blocked by
surrounding traffic)
Availability of digital maps with lane-level
description
Reliability of the visual-based integration
algorithms in non-controlled scenarios
Performance
Maturity
Cost
Challenges
Technological challenges
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5.3.2. TIGHT INTEGRATION WITH IMU
Most of mass market GNSS receivers provide the users with the estimate of their position and
velocity with a rate as high as 10 Hz. Unfortunately, one of their challenge is the availability
of reliable GNSS data in all types of environments potentially encountered, notably
constrained environments with high buildings, tunnels, foliage, interference and many other
obstacles and disturbances that alter the GNSS signal coming from satellites.
To limit these detrimental effects, it is possible to rely upon with the integration of GNSS with
INS as described in Section 3.3. The level of performance achievable with GNSS+INS
depends on the quality of the IMU incorporated into the INS, which determines how quickly
the free inertial solution, without any correction from the GNSS receiver, will drift. As
mentioned, the advent of low cost, MEMS accelerometers and gyroscopes offers the
opportunity for combining inertial navigation and GNSS for a wide variety of new road
applications. GNSS and INS can be integrated following different approaches of integration,
that, in turn, fuse different types of data and rely on different software architectures.
For instance, the LC approach uses GNSS position and velocity in a Kalman filter that models
INS error dynamics, both in terms of velocity and position. However, even if this architecture
provides some improvements with respect to standalone receivers, it is not optimal and is
very sensitive to process noise tuning. On the other hand, a typical (single GNSS receiver) TC
system accepts measurements at a lower level of processing (i.e.: pseudoranges and
pseudorange rates) from each satellite every second. Using pseudorange and phase
measurements in the integration filter allows for optimal use of any (and all) satellites that
are being tracked, even if there are less than four of them. As introduced in Section 3.3, the
TC approach cannot be considered yet the state of the art, but the interest for this type of
integration is growing due to the availability of MEMS on board of the vehicles.
5.3.2.1. Fundamentals of TC integration
Figure 5.1 shows the typical block diagram associated to a TC integration.
Figure 5.1: Typical scheme for a tightly integrated GNSS/INS system
A tightly-integrated system uses the pseudorange and pseudorange rate information
extracted from the GNSS receiver to compute the corrections to be applied to the trajectory
estimated by the INS device. Pseudoranges and pseudorange rates are also employed to
estimate, if necessary, the biases that affect the accelerometers and the gyroscopes (this is
particular important when using low cost devices, such as MEMS). Tight integration is based
on the definition of a state-space model of the hybrid system and the application of an
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Extended Kalman Filter (EKF) to compute the corrections necessary to refine the INS-based
trajectory.
It is worth noticing that, in the tight integration case, the GNSS information is used as a
refinement of the INS information: the GNSS information is used to counteract the intrinsic
derivation of the INS solution, correcting the INS trajectory. Pseudorange prediction and data
fusion through the EKF are generally software routines running on a microprocessor. Tightlyintegrated systems improve the navigation performance, mainly in terms of positioning
robustness, but as a counterpart, this approach adds complexity, which usually means more
computing power, more complex algorithms and software.
According to the authors’ experience, the expected position accuracy of a LC system
composed of low cost devices, in open sky conditions, is around 2 meters most of the time.
Through simulation, we evaluated the performance of the LC against the TC in case of partial
GNSS signals outages (i.e. number of satellites less than four, but not all obscured). While
the LC diverges (i.e.: error higher than 50 m) after 20 seconds of GNSS signals outage, the
TC provides positions for more than 60 seconds, with errors approximately lower than 35 m
with 2 satellites tracked, and 25 m with 3 satellites tracked. The simulation proves that when
the number of satellites is less than four, the LC technique can only rely on the INS device
while the TC is still able to provide an acceptable solution by exploiting the measurement
coming from the few satellites in view.
As an additional example, Figure 5.2 shows the improvement of the TC versus a standalone
mass market receiver in a real case. The blue marks refer to the positions estimated by the
TC architecture implemented on a real time embedded system. The red marks are the
positions estimated by a standalone mass market receiver. For a fair comparison of the
performance, we used the same antenna for both the embedded system and the receiver.
(a)
(b)
Figure 5.2: Comparison between GNSS standalone positioning (blue) and TC integration between GNSS
and IMU (red). (a) passage through a city tunnel and (b) parallel street lanes with trees in the
surrounding
Figure 5.2 (a) shows that the TC provides accurate positions, when the driver passed through
a tunnel. Clearly, the standalone receiver cannot handle such an outage and provides
positions affected by an error on the order of 20-25 meters. Even if in this case a map
matching algorithm could be able to smooth the error out, there are situations in which small
errors are amplified by the map matching. An example is reported in Figure 5.2 (b). Errors on
the order of 10 meters (see the blue marks in the middle of the figure) can induce the map
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matching to place the vehicle in a parallel street with respect to the real one. This is more
likely in street surrounded by trees and buildings. On the other hand, yaw and velocity
constraints on top pf the TC allows for a smooth vehicle’s tracks, composed by accurate
positions (see the red marks).
Even if an increased positioning robustness is not perceived as a need by most of the users
relying on GNSS for self-navigation, some road applications require accurate position
estimates with high availability rate. This is the case of systems for the provisioning of
routing guidance and alerting when the vehicle is approaching large road attributes (e.g.
roundabout, bus stop). The user requirements associated to this system call for an on board
unit able to estimate positions with an accuracy below 5 meters, at least 98% of the time.
This level of performance are expected to be not fully ensured by mass-market standalone
GNSS receivers (especially in urban environment), but a tight integration between GNSS and
INS seems a viable solution to match the users requirements and handle GNSS signal
outages on the order of tens of seconds.
5.3.2.2. Technological Issues of Tight Integration with IMU
In this section recalls the most challenging signal processing aspects that characterize the
implementation of a GNSS/INS system. These must be considered to guarantee a proper and
efficient realization of the TC algorithms:

GPS/INS synchronization: Generally, GNSS receivers provide the internal 1PPS signal
(a 1-Hz pulse) as output. Whereas this signal can be considered absolute for sensor
synchronization purposes, IMUs use their own clock (nominally 100Hz), providing
sensor data samples not aligned with the GNSS time reference. The design of a
synchronization circuitry between the receiver and the sensors is critical and is at the
basis of any algorithm fusing raw GNSS and INS measurements.

INS calibration: The accurate calibration of the sensors is fundamental for the
determination of the systematic errors. While the calibration of accelerometers can be
performed off-line, the calibration of gyroscopes has to be performed in real-time
under static initial conditions. Since an initial calibration could not be implemented in
real system, alternative solutions (e.g.: the periodic gyro calibrations on the run) have
to be considered.

Effects of vehicle’s vibrations: the IMU’s raw data are not only corrupted by noise
generated by the INS sensors but they are also affected by the additional noise
created by the vehicle (e.g. vibration). In order to use the accelerometers and
gyroscopes in a proper way a de-noising filter (e.g. low-pass filter, wavelets etc.) has
to be applied. This operation adds complexity.

Implementation of inertial mechanization equations: this issue becomes particularly
important when running a TC architecture in real-time. Every IMU provides
accelerometers and gyros measurements at a nominal rate at least of 100 Hz. These
measurements are then used to calculate position, velocity and attitude. An efficient
real-time implementation requires the split between low and high rate processing, in
order to limit the computational burden of the algorithm.

INS additional constraints: During GNSS signal outages, the vehicle’s positions can be
provided by the INS only. The residual noise can still affect the accuracy of INS
solution, inducing a drift after a certain amount of time (e.g. it depends on the quality
of INS gyroscopes and accelerometers). Therefore, in order to prevent the INS
solution from such drifting additional constraints have to be included. For instance,
Non Holonomic Constraints is a common method for vehicular applications and it is
quite effective in reducing the positional error generated by the standalone IMU.
Again, this strategy adds complexity although it helps to improve performance.
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5.3.2.3. Perspectives toward the road sector
Leveraging on the progress in the field of sensors technology, low cost MEMS are now used in
applications where the requirements in terms of package dimensions and costs are stringent.
MEMS are already installed in some IVS and combined in LC with GNSS receivers (see Section
3.3.1). The data fusion of GNSS and MEMS measurements at a deeper level is a promising
approach that system integrators and suppliers should carefully consider.
Criteria
Score
Justification
M/H
Recent on field tests demonstrated that the
increased robustness of TC GNSS/INS systems
results into an increased accuracy, especially in
environments with trees and short tunnels
Position authenticity
M
GNSS receiver coupled with an IMU provides
protection by effectively cross-checking the
receiver’s velocity estimates with the integrated
IMU’s acceleration measurements
Robustness to interference
M
In case of short GNSS signal outages (i.e: order of
tens of seconds) due to a jamming attack, the
navigation system is able to provide positions
leveraging on the IMU
Availability
M
TC GNSS/INS systems increases the availability of
the positions estimates, because the integrated
system is able to compute the PVT even when the
number of satellites is lower than 4.
Position integrity
H
TC GNSS/INS systems increase the position
accuracy and mitigate the effect of NLOS signals.
This, in turn, reduces the probability that the
positioning error is higher than the PL.
M/H
Given the system available at a certain instance, TC
integration provides position estimates even during
subsequent GNSS signal outages, thus increasing
continuity
Technological readiness
M/H
Although some parts of the signal processing are
still complex and require adequate resources, TC
GNSS/INS solutions have been demonstrated in
real time with prototypes embedding low cost
GNSS receivers and sensors.
Time-to-market
L/M
Results available in literature are promising and
most of the complexity is associated to software
routines (i.e.: integration through Kalman, sensors
calibration, de-noising, non-holonomic constraints,
etc.). Therefore, it is expected that new products
will consider TC integration in place of LC
architectures.
L/M
Considering the cost of mass-market receivers and
sensors, the overall cost of a TC GNSS/INS system
could be estimated in the range of [500-1000]
Euro.
L
It is expected that the current costs of TC
GNSS/INS systems will decreases, with the
Performance
Position accuracy
Continuity
Maturity
Cost
Estimated cost in 2015 (current)
Estimated cost in 2025
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availability of more powerful boards,
microprocessors and sensors at limited costs.
Challenges
Technological challenges
L/M
TC integration adds complexity to LC approaches.
Some part of the signal processing requires careful
design and test (i.e.: calibration, mitigation of
vibrations, implementation of inertial
mechanization equations)
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5.4. MITIGATION OF STRUCTURED INTERFERING SIGNALS
Currently, there is a tremendous need to design GNSS receivers robust to structured
interferences (i.e. RF spoofing attacks), since they are often employed in more complex
systems. The ability to authenticate the estimated positions is an important feature of future
LBSs for liability-critical applications. The authentication process is based on specific
processing of the received GNSS signals and involves the detection, and possibly the
mitigation, of structured RF interference over bands allocated to GNSS. Such structured
interference refers to the transmission of false GNSS signals intended to deceive location
processing into reporting false data [RD53]. If not detected, RF spoofing deceives GNSS
receivers and causes the LBS to provide a location not associated with the actual user’s
position, but instead provides the location dictated by the spoofing signals without any notice.
The next generation of GNSS receivers will likely embed algorithms for the authentication of
the positions. From a general perspective, RF spoofing can be tackled either with
(i)
new authenticated civilian GNSS signals and
(ii)
countermeasures implemented at the receiver level.
The two are complementary and will be presented in the following Sections (5.4.2
Authenticated civilian signals, 5.4.3 Spoofing countermeasures for standalone GNSS
receivers), after a short introduction of the risk associated to spoofing attacks.
5.4.1. SPOOFING RISK
The RF self-spoofing is the most likely and most dangerous type of attack for many liability
critical applications in road. As an example, let’s consider a system for on street parking
billing that charges users on the basis of the real time spent at the parking lot. Let’s us
assume that the system estimates the cars position with GNSS, through on-board tags that
send data to a control centre. Obviously there is a direct interest of the users to spoof the
GNSS receiver inside the tag, since they would have a direct economic advantage with false
positions outside the parking lot sent to the service provider.
As demonstrated by some scientists in recent publications [RD54] [RD55] [RD56] [RD57], it
is not difficult to induce a mass-market receiver to compute erroneous positions, for example
through simple meaconing or a simplistic spoofing attack [RD58]. Although some believe that
the complexity associated with a spoofing attack is too high and makes the attack unlikely,
the situation is rapidly evolving. In fact, advances in computing power will make feasible
attacks carried by simple software downloaded from the internet. Such software, when
combined with a relatively simple front-end design, can be used to launch highly effective
attacks against the civil components of the GNSS signals at the receiver level [RD59].
Obviously, the effects of spoofing attacks against GNSS receivers are mitigated using
terrestrial technologies as back up. In fact, Dedicated Short Range Communication (DSRC) or
video cameras can be used to validate the data coming from GNSS sensors. However these
systems could be expensive and not always applicable. Under this perspective, any method
that make current receivers more robust to intentional interference can contribute to reduce
the spoofing risk, providing the required level of reliability and reducing the costs of
enforcement systems.
Nonetheless, it is important not to forget that the GNSS signal is not the sole weak link of the
“trustable position chain” of an LBS. The communication channel between the user and the
service provider, in particular at the data encoding layer, may be subject to forgery as well.
For example, if the LBS relies on smartphones, spoofing attacks can be carried out after the
GNSS sensor without the need to generate false GNSS signals. For instance malicious
software installed on rooted smartphones can mystify the (authentic) positions computed by
a trusted GNSS receiver before they are transmitted to other users or to a control centre.
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5.4.2. AUTHENTICATED CIVILIAN SIGNALS
A simple definition for GNSS signal authentication is given in [RD60] as the “certification that
a received signal is not counterfeit, that it originates from a GNSS satellite and not a
spoofer.”
As detailed in [RD61], the concept of signal authentication represents a cryptography-based
countermeasure to possible spoofing attacks. It requires the presence of a cryptographically
secure portion in the received signal (sometimes referred as a security code or digital
signature) and it involves two subtypes of authentication:

Code origin authentication: certification that the security code originates with the
GNSS control segment (i.e., source authentication);

Code timing authentication: certification that the security code arrives promptly (i.e.,
with the correct time of arrival) and intact (i.e., data integrity).
Unfortunately, position authenticity cannot be ensured by the current standalone receivers,
which solely exploit the currently available civil GNSS signals [RD62]. In fact, baseline signals
currently broadcast by satellites (e.g. GPS L1 C/A and Galileo E1 OS) do not include any
cryptographic protection related to their origin (satellite or spoofer) and time of arrival.
Alternative solutions have recently been proposed aiming to overcome these limitations (e.g.
see [RD63] [RD64] [RD65]). The idea is to exploit not only the civil signals, but to try to take
advantage in some way of the hidden attributes of restricted-access GNSS signals. In fact
these signals are intrinsically more robust against possible spoofing attacks, since it is difficult
(and/or expensive) for an attacker to generate plausible counterfeit replicas of them (due to
the presence of Navigation Message Encryption – NME – and/or Spreading Code Encryption –
SCE). However, these alternative solutions try to take advantage of these cryptographic
features in unconventional ways, for example by cross-comparing the raw received signal
samples at two different locations (a reference server and a user/client). For this reason,
these approaches are unsuitable to standalone receivers, since they need a data connection
in order to send raw data to a remote authentication server, further increasing the
implementation cost and complexity.
Other interesting proposals have been made for the design of standalone authentication
solutions for civil applications based solely on open-access GNSS signals. For example,
modifications of the current civil signal in space (or at least of the navigation message
content, including some sort of digital signature in the navigation data stream) have been
recommended both for the modernized GPS
and the Galileo Open Service signals
[RD66][RD67][RD68]. Thus, it is reasonable to expect that a position authentication
mechanism will be provided in the near future within the GNSS signal itself, as an added
value of the GNSS system [RD62].
In this context, some recent works [RD67][RD69] show that Galileo can achieve very good
performance, including the possibility to authenticate the navigation messages of other GNSS
constellations. In detail, a basic authentication capability is foreseen for the Galileo Open
Service, based on the idea of authenticating the satellite navigation messages by means of
digitally signing the navigation data (i.e. Navigation Message Authentication – NMA) and thus
keeping the navigation message clear (i.e., unencrypted). This basic authentication solution,
together with the more robust cryptographic authentication provided by the access-controlled
signals of the Galileo Commercial Service (by means of NME and/or SCE, as explained in
[RD69]), represent key differentiators of Galileo with respect to other systems.
It must be remarked that the exact definition and implementation of such authentication
services is yet to be finalized and, especially for the Galileo Commercial Service, these
technical aspects will depend on the EU member‘s agreement and the involvement of
external service providers [RD69]. However, as soon as these authenticated signals will be
available, they will be clearly suitable to vehicular applications, allowing future On-Board
Units (OBUs) to perform a standalone assessment of the authenticity of the computed
Position, Velocity, and Time (PVT) solution and decreasing the need for costly additional
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sensors or other countermeasures to spoofing attacks. In addition, different service levels for
the positioning authentication will be available, for example through the basic Open Service
authentication: this choice will allow ensuring a sufficient robustness against potential
spoofing attacks, depending also on the user needs, the application requirements, and the
relevant regulations (e.g. Digital Tachograph, eCall).
5.4.2.1. Technological issues of signal authentication for road users
The readiness of the market to the application of authentication mechanisms is expected
depend not only on need of services enabled by authenticated signals, but also on the cost
the final user for the technological upgrade. Such technological upgrade, necessary
enable receivers with authentication capabilities, could be an issue for certain types
receiver architectures.
to
to
to
of
Focusing on the applicability of the signal authentication to road applications, it is important
to point out that from a general point of view, current GNSS-based navigation systems for
vehicles can rely on two different types of On-Board Units (OBUs):
1. the first is integrated in the vehicle dashboard and is power supplied by the vehicle power
network. The GNSS receiver embedded in the OBU has no constraints related to power
consumption, thus it constantly tracks the signal in space and decodes the navigation
message that also contains the information useful for its authentication (i.e. digital
signature bits).
2. the second is not integrated in the vehicle dashboard and can be seen as a black-box
power supplied by batteries.
In the second case, mass market devices might implement power saving algorithms, when
working without network power supply [RD70]. Such algorithms, described for example in the
patent [RD71], are based on two different receiver working states:


an active state, in which all the receiver’s parts are activated, like in a standard
receiver,
and a sleep state, in which the RF module, the baseband and the DSP core are
switched off.
By similarity to a square wave (see Figure 5.3), sometimes these types of algorithms are also
named duty-cycle tracking.
Figure 5.3: Example of Duty-Cycle tracking scheme
According to [RD71], the sleep period (OFF state) length is programmed based on the
receiver dynamics, mainly acceleration and speed, estimated during the latest active (ON)
state.
The different tracking strategies of mass-market receivers have a non-negligible impact on
the implementation of the GNSS signal authentication. In fact, if the GNSS receiver is power
supplied by the vehicle, it can constantly decode the navigation message, including the
information useful for its authentication. On the contrary, if the mass market receiver
implements a duty-cycle tracking scheme, the active/sleep state parameters have to be set,
by also taking into account the specific SIS authentication characteristic (i.e., the sleep period
cannot be set in correspondence of the reception of the digital signature for Galileo Open
Service authentication).
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As demonstrated by this simple example, new signal formats enabling position authentication
might have an impact on the design of the algorithms and architectures of GNSS
receivers, mainly if they have to respect other constraints, such as low power consumption,
high sensitivity, high number of channels, etc.
Finally, the client-server authentication approaches introduced in Section 5.4.2 are affected
by several drawbacks, especially when considered for vehicular applications. First of all, they
are not suitable for standalone receivers, since the connection to a remote server is
mandatory. In addition, snapshots including raw signal samples should be collected at the
receiver, possibly at pre-defined time slots and with a wide bandwidth, in order to correctly
receive the restricted-access GNSS signals. In addition, a data connection with sufficient
throughput is required in order to transfer the snapshots to the server. These aspects
increase the complexity and the cost of the client/receiver with respect to a consumer-grade
standalone device.
5.4.2.2. Perspectives toward the road sector
Galileo will broadcast authenticated civil signals on two frequency bands, the E1 (OS
authenticated signals, expected in the near future as an added value feature with respect to
baseline OS signals) and E6 (CS signals). Authentication from satellites is the first barrier
against RF spoofing, therefore Galileo will contribute to make liability-critical LBS more robust
to intentional attacks. Note that new authenticated signals are designed to guarantee
compatibility with existing devices and signals.
As reported in very recent publications [RD69], “the re-profiling of the Galileo Safety Of Life
(SOL) in the early 2010 was an important event for the Galileo CS”. Therefore, even if an
Interface Control Document (ICD) of the authenticated E1 and E6 signals has not been
released yet, many believe that an intense effort is being carried out to have the signal
transmitted in the next years.
Criteria
Score
Justification
H
The availability of authenticated civilian GNSS
signals represents a cornerstone for assuring that
the position is derived from true (not counterfeit)
satellite signals.
Technological readiness
M/H
Approaches based on client-server architectures
are already available in the form of prototypes or
ready-to-market services (high maturity).
Interesting standalone authentication solutions
based solely on civil GNSS signals have recently
been proposed (medium maturity, proof-of-concept
level).
Time-to-market
L/M
Ready-to-market client-server services are
available. On the other hand, the time to market of
standalone authentication solutions depends on
when the definition of the authenticated signals will
be finalized.
Estimated cost in 2015 (current)
M/H
Currently available client-server authentication
approaches increase the complexity and the cost of
the client/receiver with respect to a consumergrade standalone (unauthenticated) device.
Estimated cost in 2025
L/M
A reduction of the receiver cost can be foreseen in
Performance
Position authenticity
Maturity
Cost
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the near future, as soon as civil authenticated
signals will be available. Solutions based on these
signals will decrease the cost for additional sensors
or other anti-spoofing countermeasures.
Challenges
Technological challenges
Framework conditions
(i.e. regulation and standards)
M
A technological upgrade is necessary to enable civil
receivers with position authentication capabilities.
The authentication might have an impact on the
design of the algorithms and architectures of GNSS
receivers, implying coexistence issues (e.g. dutycycle tracking).
M/H
The ICD definition and the implementation of
Galileo authentication services are yet to be
finalized in the following months.
5.4.3. SPOOFING COUNTERMEASURES FOR STANDALONE GNSS RECEIVERS
Together with the design of encrypted GNSS signals for civilian use, there is a growing
interest towards standalone receiver-based defences that process the received signal and
determine if it is genuine or not. Such interest is clearly driven by the fact that GPS, currently
the most used GNSS, does not incorporate authentication means in its civilian signals, due
both to institutional priorities and to long procurement and deployment cycles [RD72].
Generally, most of anti-spoofing techniques for standalone receivers work at the base band
signal processing level and just aim at detecting possible false signals, without attempting to
mitigate or remove them. A very clear and detailed analysis on spoofing countermeasures
was provided by the authors of [RD73], where some techniques were roughly classified as
spoofing detectors (i.e.: they discriminate the presence of spoofing signals, without
necessarily mitigate the effect of the attack) and spoofing mitigation (i.e.: they attempt to
neutralize the detected spoofing signals, restoring the correct positioning capabilities of the
receiver).
Several types of countermeasures have been proposed with different characteristics in terms
of complexity, performance and cost. Considering liability-critical applications, if the LBS does
not use enforcement systems to validate GNSS-related data, there might be the risk that the
LBS is not protected from self-spoofing attacks. Therefore, any countermeasure against
spoofing assumes a role of primary importance.
Navigation systems integrated in the vehicle dashboard evidently offers some advantages, as
the GNSS receiver can be easily coupled with external sensors and the consistency check with
other solutions is actually an effective strategy to detect false positions [RD73]. With this
anti-spoofing technique, the receiver compares the solution extracted by the received GNSS
signals to other position and navigation solutions and the augmenting data from auxiliary
devices such as inertial measurement units (IMUs) helps the receiver to discriminate the
spoofing threat. In this specific case, the sensors outputs (e.g., acceleration and angular
velocity in the case of IMU) are compared with the navigation solution and the consistency
can be verified.
For navigation systems not integrated in the vehicles (e.g.: OBUs relying on standalone GNSS
receiver, smartphones, etc.), methods for signal authentication are fundamental. Antispoofing techniques suitable to standalone GNSS receivers are based on [RD61] [RD57]:

measurements consistency checks;

methods borrowed from the Signal Quality Monitoring (SQM);

spatial signal processing (i.e. direction of arrival comparison with an antenna array,
pairwise correlation in a synthetic antenna array, multi-antenna beam forming and null
steering);
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
time of arrival discrimination (focusing on the PRN code and data bit latency in spoofing
signals);

distribution analysis of the correlator outputs (monitoring possible fluctuations due to the
interaction between the authentic and spoofing signals);

vestigial signal defence (based on the fact that the spoofer generally does not suppress
the authentic signals)

Receiver Autonomous Integrity Monitoring (RAIM, suitable to detect anomalies in
pseudorange measurements).
The list is not exhaustive and reports only the methods most known in literature.
5.4.3.1. Technological issues of spoofing countermeasures for standalone
receivers
At the time of writing, low cost receivers for road applications do not feature spoofing
mitigation techniques. The complexity of the countermeasures is certainly the main key factor
to consider in the design of the next generation of receivers, because they are thought for
consumer grade devices with limited cost.

Measurements consistency checks can prevent the most simple spoofing attacks (i.e.:
direct spoofing attacks) and add a negligible level of complexity to the conventional
processing. However, their effectiveness is limited, because they fail against the selfspoofing intermediate attack, that is considered the most likely risk in road liability
applications.

Methods based on spatial signal processing (i.e. antenna arrays) or vestigial signal
defences shows very good performance in terms of protections against spoofing attacks.
However, they are rather complex, require additional hardware and heavy computational
burdens and are generally proposed for high-end receivers to be used in applications such
as aviation.

A good compromise between performance and complexity is represented by the methods
borrowed from the SQM. However, these methods do not always discriminate false GNSS
signals and multipath and might trigger false alarms, mainly in urban environments,
where the presence of multipath is likely. The performances of these methods are
promising, but a clear assessment of the methods in terms of a high Probability of
Detection and low Probability of False Alarm needs to be proved.

The use of back-up technologies to validate the GNSS data seems appropriated for road
applications, because several sensors not based on GNSS are available on board of the
vehicles. Examples are IMUs and odometers, GPRS/3G modems and in general any
communication device that could be used for positioning.
From a general perspective, we believe that the signal authentication proposed for the Galileo
E1 OS is a viable solution to strongly reduce the risk of spoofing attempts in liability-critical
LBS. It requires few changes (at software level) of the conventional GNSS receiver
architecture and reduces the need of additional anti-spoofing standalone techniques.
5.4.3.2. Perspective towards the road sector
Spoofing countermeasures at the receiver level are complementary to signal authentication
and contribute to make GNSS receivers more robust against intentional interfering signals.
Complex techniques, such as the antenna array are still too expensive (even if they provide
excellent performance in terms of position authenticity). On the other hand, in the short term
receiver manufacturers will likely consider methods based on data fusion from other sources.
Examples are the integration with INS, odometers and the use of signal of opportunity as well
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as the integration with cellular network. Techniques based on advanced signal processing,
implemented through a new software release, are of interest for low cost, mass market
receivers. Recent facts demonstrated how self-spoofing is relatively easy with low cost
equipment. Thus, the integration of spoofing countermeasures in mass-market receivers will
be likely in the upcoming years.
Criteria
Score
Justification
M
From a general perspective, any method for RF
spoofing mitigation increases the level of
reliability of the estimated PVT
Technological readiness
M
Some of the countermeasures proposed in
literature show promising performance, but they
are too complex (i.e.: antenna arrays), requiring
additional hardware components and cannot be
used in low cost applications.
Other techniques based on advanced signal
processing have been demonstrated in receiver
prototypes/SW receivers and can be considered
more mature for practical applications.
The lack of minimum performance requirements
in terms of position authenticity for SCA and
LCA can be considered a limit.
Time-to-market
M
The time-to-market depends on the specific type
of countermeasure. It is expected that as soon
as the risk of RF spoofing will be perceived as
real, new products will start featuring basic, but
effective, countermeasures.
N.A
State of the art mass market receivers do not
feature advanced spoofing countermeasures,
mainly because the RF spoofing is not yet
perceived as a real threat.
L
Already in a few years, it is expected that massmarket receivers will be able to receive
authenticated GNSS signals (i.e.: the Galileo
authenticated OS), complementing the ability of
mitigating RF spoofing attempts with advanced
signal processing, mainly through data fusion
with other sensors.
The associate cost is considered low, as most of
the improvements are expected from new
software routines.
Technological challenges
M
At the moment, most effective anti-spoofing
techniques are too complex and expensive.
Framework conditions
(i.e. regulation and standards)
M
Missing performance bounds in terms of
probability of detection / probability of false
alarm to use during the assessment of
standalone countermeasures.
Performance
Position authenticity
Maturity
Cost
Estimated cost in 2015 (current)
Estimated cost in 2025
Challenges
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5.5. INTEGRATION WITH SIGNALS OF OPPORTUNITY
GNSS receivers are widely used in many applications for positioning, navigation and timing,
where the users are often located in urban or indoor areas. However these GNSS hostile
environments provide new challenges, due to the massive buildings and obstructions, which
make the view of open sky almost impossible. Therefore the GNSS only positioning will
experience degraded performance in terms of availability, accuracy and reliability.
Fortunately in these areas many local networks are implemented and deployed, such as for
example digital television signals, cellular networks, and Wi-Fi. They were originally designed
for other purposes, but they can be used for positioning, thanks to some of their properties,
such as a high Signal-to-Noise Ratio (SNR) [RD74]. For this reason, they are generally
denoted as Signals of Opportunity (SoO) when exploited for positioning purposes [RD75].
Some recent efforts have been done to integrate SoO and GNSS or to replace GNSS signals
when not available. However, it must be pointed out that there is no single alternative
that represents a true replacement for GNSS location capability, but there are many
that can complement and augment satellite-based systems well. Some examples of
different technologies and approaches that can be combined with GNSS to create a system
meeting requirements that are not achievable by GNSS alone are provided for example in
[RD76] and references therein.
Among possible alternative solutions, location technologies based on RF signals represent
more flexible approaches with respect to non-RF approaches, which in most cases feature a
limited transmission range of the signals (e.g. ultrasound, vision, infrared, smart
floors/furniture). For example, ultrasound-based systems can have very fine precision but are
strictly limited to line of sight operation and highly subject to environmental noise [RD77]. On
the other hand, RF propagation can be used for close-in applications as well as those applied
over hundreds of kilometres. However, in many cases where RF has superior range it does so
at the expense of precision.
Numerous types of RF location technologies have been implemented over the years and
can be break down into five major types:
1. Proximity-based approaches, including contact and near-contact sensors (e.g. RFID);
2. Direction-finding or angle-of-arrival, where two or more receivers can be used to
triangulate on the two-dimensional horizontal location of the transmitter;
3. Doppler, exploiting the frequency shift on the received signals (Doppler effect) in order
to estimate the relative velocity and then distance between receiver and transmitters;
4. Signal strength, where the estimated signal power (or other signal-based metrics) are
used to form an estimate of the range, often referenced to previously stored location
information in a database (otherwise known as RF fingerprinting);
5. Timing or phase, using measurements of the received phase of the RF signal or an
additional timing modulation on the signals to estimate the range between transmitter
and receiver (as in the case for GNSS).
Further details on these different approaches are provided in [RD76], where the following
technologies and their integration with GNSS are also discussed:










RF IDentification (RFID),
Bluetooth (IEEE 802.15),
Wi-Fi (IEEE 802.11 b/g/n),
ZigBee (IEEE 802.15.4),
Terrestrial network-based systems - cellular networks (2G, 3G, 4G LTE),
Ultra-WideBand (UWB),
Pseudo-satellites (or pseudolites),
Indoor GNSS repeaters (or synchrolites),
Self-synchronizing networks (e.g. LocataLite),
Digital television signals.
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Since digital television signals are often mentioned as one of the most promising
option for the exploitation as signals of opportunity, next section will focus on them.
After that the integration with terrestrial network-based systems, which also represents
another interesting solution for road applications, will be considered in Section 5.5.2.
This chapter ends with a discussion on technologies related to Dedicated Short Range
Communications (DSRC), because together with GPS receivers and satellite radio, DSRC
appears an essential component for automotive industry [RD79].
5.5.1. DIGITAL TELEVISION SIGNALS
5.5.1.1. Fundamentals of Digital Television Signals
Despite the underlying technical challenges, the following benefits and key advantages can be
anticipated for the exploitation of Television (TV) signals for positioning purposes [RD76]:

TV channels are broadcast all over the world;

They contain significantly more power (over 40 dB more) compared to GNSS
signals at the surface of the Earth;

TV signals are transmitted at a lower frequency than GNSS signals, giving better
structure penetration (pass-through walls);

They occupy a relatively high bandwidth (e.g. 6 MHz);

All digital and analog TV standards contain frame synchronization codes, thus
they can be potentially used for positioning with some adjustments on TV receivers,
treating TV transmitting towers as GNSS pseudo-satellites on the ground;

This solution also offers frequency diversity, because TV signals are allocated on
different bands with respect to GNSS.
Furthermore, their integration with GNSS receivers is expected to reinforce both systems and
open a door to universal positioning services, covering anywhere and anytime [RD78].
For digital television four different protocols exist, as shown in Figure 5.4. They are Digital
Video Broadcast-Terrestrial (DVB-T), Advanced Television System Committee (ATSC),
Integrated Services Digital Broadcasting-Terrestrial (ISDB-T), and Digital Terrestrial
Multimedia Broadcasting (DTMB).
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Figure 5.4: Digital terrestrial television systems worldwide (source: [RD80]).
In Europe and some other countries, the DVB-T is used as the standard for digital television,
including its extension called Digital Video Broadcast-Second Generation Terrestrial (DVB-T2).
DVB-T signals are located in Very High Frequency (VHF) and Ultra High Frequency (UHF)
bandwidth, actually 174−230 MHz and 470−862 MHz. The 470−862 MHz frequency band is
also associated to DVB-Handheld (DVB-H), which is designed for mobile users and derived
from the DVB-T standard. DVB-T signals are much more used and available than DVB-H
[RD75].
The DVB-T standard adopts the Orthogonal Frequency Division Multiplexing (OFDM)
technique. Therefore the whole bandwidth is divided into many subcarriers, in which the pilot
subcarriers are included. These pilot subcarriers can be used to estimate the ranges
between the receiver and different emitters with a mechanism similar to the one used by
GNSS receivers. Since the SNR requirement for ranging is much lower than the one required
by the television service, the receiver is able to see several emitters in one point. If three or
more signals are successfully processed, the receiver can provide a DVB-T only the
positioning; otherwise, it can be used to assist GNSS [RD74].
Two different network types can be deployed for broadcasting DVB-T signals [RD81]:

Multi-Frequency Network (MFN) and

Single Frequency Network (SFN).
In MFN, different emitters transmit the same signal on different frequencies, and they are not
exactly synchronized; while in SFN, all the emitters simultaneously transmit the same signal
in the same frequency. The synchronization is typically provided by some professional
GPS timing receivers, and this represents a key point when DVB-T signals are used for
positioning purposes.
5.5.1.2. Technological Issues of Digital Television Signals
The following issues related to the use of DVB-T signals for positioning must be pointed out:

The transmission of the DVB-T emitter identifier (ID) is optional;

Accurate knowledge of locations of DVB-T emitters is needed (e.g. from
map/database);

Achievable accuracy performance is limited in highly challenging urban scenarios.
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Focusing on the first point (optional transmission of the emitter ID), it must be noticed that
this is not an issue for the provision of the DVB-T service, but it is a problem to be solved
when the signals are used for positioning. Some companies (for example, RAI in Italy)
already transmit the emitter ID within their bit streams, which are then completely equal
apart from this small difference. This causes a (very limited) penalty, but is very useful for
network management and control. It is clear that this extra-information can be very useful
also for positioning purposes, since it highly simplifies the association of each received DVB-T
signal to the corresponding emitter [RD74].
Some actions could be adopted in the future in order to convince all the network
operators to transmit the emitter ID. These actions would foster the adoption of DVB-T
signals for positioning applications. In fact, the availability of the emitter ID and the
knowledge of the locations of the DVB-T transmitters (e.g. from a map or a database) would
increase the potential of hybrid GNSS/DVB-T approaches for a seamless positioning service in
urban/indoor areas, even making feasible standalone solutions based on DVB-T signals only.
Even though the emitter ID is not available, other alternative approaches have already been
proposed and investigated. A possible positioning method suitable to DVB-T Single Frequency
Networks has been presented in [RD74]. In this case, a hybrid GNSS/DVB-T receiver is
assumed, where GNSS signals are used in a first initialization phase to solve the ambiguities
referring to the various DVB-T emitters (not broadcasting their IDs). When this phase is
completed, the user position can be obtained by using DVB-T signals when the user enters a
GNSS-blocked area. The method has been tested by simulation in a dynamic scenario,
demonstrating that a mean position error as good as 6 m can be achieved if the user can
correctly associate the signals to the DVB-T emitters.
Another alternative architecture for exploiting TV broadcasts can be based on a client-server
approach [RD76]. A reference receiver in a local monitor station can measure the signal
timing of the television signals and GNSS signals, reporting this information to a location
server. The mobile receiver to be located measures the Time Of Arrival (TOA) of the television
and GNSS signals and forwards this to the location server to compute a range-based solution.
In a system implementation, combined TV+GPS location accuracies in a highly
challenging urban environment have been reported in below 50 m CEP [RD78]. Due
to such achievable accuracy performance, even if limited in challenging scenarios, it would
represent an appealing approach for applications focusing on high availability, requiring
seamless outdoor-indoor positioning capability, but with relaxed accuracy
requirements (e.g. logistics).
5.5.1.3. Perspectives toward the road sector
The results presented in previous sections (e.g. see [RD75] and references therein) allows us
to conclude that hybrid solutions based on GNSS and digital TV signals (especially DVB-T in
Europe) represent an interesting technological solution in the medium term. One key
element is the increasing diffusion of on-board infotainment systems, that already
include digital radio and DVB-T receivers. These are at the basis for the processing of
DVB-T signals to improve the performance of standalone GNSS receivers. In addition, DVB-T
can be a backup technology anytime GNSS signals are not available (i.e.: due to a jamming
attack), at least to provide rough estimates of the user’s position.
However, the current lack of standards is a barrier. Standardization and regulation actions
would possibly reduce the time-to-market, that at the time of writing cannot be considered
lower than 10 years.
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Score
Justification
M
Simple but effective spoofing detectors can
be based on the consistency checks between
positions computed using GNSS signals and
those obtained with DVB-T.
L/M
In case of GNSS signal outages due to a
jamming attack, the navigation system can
rely on DVB-T only to estimate the user’s
position.
M
Hybrid DVB-T/GNSS approaches increase the
position availability w.r.t. GNSS only. DVB-T
seen as a backup technology when GNSS
signals are not available.
Moderate accuracy performance in highly
challenging urban scenarios (50 m CEP).
Technological readiness
M
Proof-of-concept algorithms and solutions
already demonstrated.
Time-to-market
M
Possible regulation/standardization actions
would possibly reduce the time-to-market.
Estimated cost in 2015 (current)
M/H
Due to the need of accurate localization of
DVB-T emitters and high costs for system
engineering/deployment.
Estimated cost in 2025
L/M
Possible regulation/standardization actions
would foster the market adoption, leading to
a marginal cost for the equipment.
L/M
Accurate knowledge of locations of DVB-T
emitters is needed (e.g. map/database
obtained from a survey).
M
Hybrid DVB-T/GNSS approaches not yet
standardized or regulated.
Performance
Position authenticity
Robustness to interference
Availability
Maturity
Cost
Challenges
Technological challenges
Framework conditions
(i.e. regulation and standards)
5.5.2. TERRESTRIAL NETWORK-BASED SYSTEMS
5.5.2.1. Fundamentals of Terrestrial Network-Based Systems
The term terrestrial network-based positioning and navigation systems refers to those
location systems that use wireless technologies entirely deployed on the ground. The most
used wireless technologies of this kind are [RD13]:

Cellular networks,

Wireless systems based on Ultra-WideBand (UWB),

Wireless Local Area Network (WLAN),

Wireless Sensor Network (WSN) technologies.
Terrestrial network-based positioning systems can also be referred to as local or short-range
systems, because their coverage area is restricted to the region where they are deployed.
Thus, they differ from GNSS, whose coverage is global.
In addition, most of these terrestrial systems were designed and optimized having in mind
communication and data transmission services, but not positioning. Their use for positioning
purposes is motivated by the trend toward personal use of navigation systems associated
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with LBSs, requiring positioning devices able to seamlessly work under various, variable, and
critical conditions, such as inside warehouses, multi-storeyed buildings, underground stores
and parking, and indoor commercial and office campuses. Unfortunately, GNSS indoor
reception is dramatically impaired by strong attenuation due to walls and slabs and by the
multipath effect. Therefore, urban and indoor environments open challenging issues for GNSS
signal processing and receiver design, to which the integration with terrestrial network-based
systems try to give solution.
When there is an indoor receiver, GNSS signal reception is characterized by a strongly
attenuated direct component and several reflected or scattered multipath components. The
attenuation affecting the direct path can range from 10 to 25 dB, depending on the nature of
the concrete, thus reducing the carrier power the receiver has to deal with from about -160
dBW to even -190 dBW; however, the nominal sensitivity in signal acquisition of current
commercial receivers is around -178 dBW. Furthermore, indoor multipath and scattering
effects become far more harmful. In such conditions, the use of basic GNSS receivers is really
questionable and substantially different approaches have to be adopted.
Nowadays much research is focused on the use of terrestrial wireless technology as a means
of developing positioning and navigation systems that work where satellite systems fail. New
LBSs require a certain level of location accuracy to be met by the positioning systems, in
spite of all the propagation problems typical of wireless communication, such as channel
fading, low SNR, multiuser interference, and multipath conditions.
Pioneering work on indoor positioning dates back to more than 10 years ago, but a lot of
work is still going on to refine and get past those pioneering ideas, both in academia and
industry. Several wireless technologies have been studied for indoor positioning and their
distinguishing elements are:

The positioning algorithm, which may use various types of measurement of the signal,
such as Time Of Arrival (TOA), Angle of arrival (AOA), and Received Signal Strength
(RSS).

The physical layer of the network infrastructure used to communicate with the user's
terminal.
Among possible terrestrial network-based systems, recently there has been a large interest in
exploiting cellular networks to provide positioning services. Thus, next sections will focus
on the advantages and open issues related to the use of this specific technology in road
applications.
5.5.2.2. Positioning in cellular networks
Nowadays cellular networks are widely deployed in all developed countries, relying on a set of
base stations. Each Base Station (BS) covers a cell, with a coverage radius up to about tens
of kilometres.
Such cellular networks can be used for positioning purposes, without the necessity to deploy
ad hoc and expensive wireless infrastructures. Unfortunately, 2G and 3G cellular standards
were designed and optimized having in mind data and voice communication services but not
positioning.
The simplest method to obtain some coarse location information is by using the Cell
IDentification (Cell lD) as proximity indicator. The localization accuracy will be of the order of
the cell size, enough for some applications if small and densely distributed cells (picocells) are
deployed. For larger cells, some more elaborate techniques have to be used.
Potentially, 2G/3G cellular physical layer can provide ranging information through Time Of
Arrival (TOA) estimation, even though the relatively small bandwidth limits the achievable
time resolution (e.g., 1 µs for GSM, about 200 ns for 3G systems).
The most widespread positioning technology in cellular networks is based on Time Difference
Of Arrival (TDOA). For instance, GSM location is based on the existing Observed Time
Difference (OTD). OTD evaluates the time difference between signals traveling from two
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different BSs to an MS. At least three visible BSs are needed to estimate the MS position,
obtained by intersecting hyperbolic lines having foci at the BSs' positions. The final location
estimation accuracies in GSM-based location systems using OTD ranges from 50 to 500 m.
The signal parameter estimation used in UMTS networks is the Observed TDOA (OTDOA),
which is based on the TDOA approach. Anyway, the accuracy of cellular-based
positioning is quite modest, for this reason recent location estimation algorithms try to
exploit any available information about the environment (e.g., fading conditions, Doppler
frequency, and network topology) to attain higher accuracy through data fusion
methods [RD13].
5.5.2.3. Technological Issues of Positioning in Cellular Networks
The approaches for positioning in cellular networks (2G, 3G, or 4G) can be considered as a
mature technology. In fact, such approaches are already standardized and adopted in
commercial devices for providing location services, also providing AGNSS data to the user
receivers.
However, the following issues must be remarked:

The potential location estimation accuracy achievable exploiting cellular network
signals is limited in challenging scenarios (e.g. 50 to 500 m);

These signals are impaired by typical propagation problems of wireless
communication (channel fading, low SNR, multiuser interference, and multipath);

The positioning performances are also related to the geometry of the cells and the
density of the BSs in proximity of the user receiver.
5.5.2.4. Perspectives toward the road sector
As stated in [RD33], in the coming years In-Vehicle Systems (IVS) – enabled to communicate
through
cellular
networks
will
progressively
replace
PNDs.
Integrated
navigation/communication systems are already adopted in many road applications (e.g.
fleet tracking), then it is expected that more and more applications and services will take
advantage of integrated navigation/communication services in the short term (e.g.
eCall).
Moreover, the widespread use of consumer devices (i.e.: smartphones and tablets)
embedding a GNSS chipset motivate the integration of the two technologies, that will be at
the basis of new services at limited costs. The standardization already addressed some
aspects of LBS based on GNSS and cellular networks14.
Signals from cellular networks can be also used to implement trilateration methods. In this
case, cellular networks can be a backup technology anytime GNSS signals are not available
(i.e.: due to a jamming attack), at least to provide rough estimates of the user’s position.
14
See for example http://www.etsi.org/about/what-we-do/global-collaboration/3gpp
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Score
Justification
M/H
Increased TTFF thanks to AGNSS methods.
Position authenticity
M
Simple but effective spoofing detectors can
be based on the consistency checks between
positions computed using GNSS signals and
those obtained with cellular network. Also, a
simple technique able to match the
requirements of some applications consists in
comparing the GNSS position with the area
covered by a specific network cell ID.
Robustness to interference
M
In case of GNSS signal outages due to a
jamming attack, the navigation system can
rely on cellular networks only as a backup to
estimate the user’s position.
GNSS sensitivity
M
Aiding from the cellular network can include
the navigation message data bits. This allows
for a longer integration time, that is a way to
enhance GNSS sensitivity.
Availability
M
Increased availability of (at least coarse)
positioning information w.r.t. GNSS only.
Limited location estimation accuracy in
challenging scenarios (e.g. 50 to 500 m).
Performance dependent on the positioning
method (Cell ID, OTD, OTDOA) and on the
geometry/density of the cells.
M/H
Mature technology for integrated
navigation/communication (e.g. eCall).
Good perspectives for cooperative localization
approaches (e.g. peer-to-peer).
L
Already adopted in some road applications.
L/M
Moderate increase of the cost/complexity of
the vehicle on-board equipment.
Accurate localization of BSs needed.
L
Marginal cost in future vehicles (always
connected to wireless networks).
M
Cellular signals impaired by typical wireless
propagation problems (channel fading, low
SNR, multiuser interference, and multipath).
Improvements on the achievable positioning
performance expected/required in next
generation cellular networks.
L/M
Already standardized approaches and signals.
Pressing needs for road applications,
requiring better positioning performance.
Performance
TTFF
Maturity
Technological readiness
Time-to-market
Cost
Estimated cost in 2015 (current)
Estimated cost in 2025
Challenges
Technological challenges
Framework conditions
(i.e. regulation and standards)
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5.5.3. DEDICATED SHORT RANGE COMMUNICATION (DSRC) TECHNOLOGY
5.5.3.1. Fundamentals of DSRC
DSRC provide communications between vehicles and the roadside in specific locations. DSRC
are used at the roadside for vehicle applications such as tolling, smart parking systems, and
between vehicles [RD77]. This last is for more advanced concepts, still under investigation, to
support adaptive cruise control.
DSRC are for data-only systems and operate on frequencies allocated in the microwave
region (i.e.: between 5,725 MHz to 5,875 MHz) and as such they use conveniently small
antennas, suitable for easy integration into small tags. The technology is part of what can be
considered to be RFID, since a principal function of the tag is usually to identify itself for
validation. Roughly speaking, DSRC systems consist of the Road Side Units (RSUs) and the
On Board Units (OBUs) with transceivers and transponders. The DSRC standards specify the
operational frequencies and system bandwidths, but also allow for optional frequencies which
are covered (within Europe) by national regulations.
DSRC systems are used in the majority of European Union countries, but these systems are
currently not totally compatible. Therefore, standardization is essential in order to ensure
pan-European interoperability, particularly for applications such as electronic fee collection,
for which the European imposes a need for interoperability of systems. EGNSS, being a space
technology with global coverage, helps to overcome the system incompatibility. Commercial
OBUs with DSRC integrated with GNSS are already available on the market, see for example
[RD82]. For road tolling the use of GNSS enables migration from, and interoperability
with, existing DSRC based charging systems.
In this context, the standardization of architectures and protocols is very important, because
it ensures compatibility and interoperability within a multi-vendor environment. The base
standards for DSRC have been developed by CEN [RD83]. The ETSI work on DSRC
complements the CEN activity in response to the European Commission Mandate (M/338 issued to ETSI, CEN and CENELEC) in support of interoperability of electronic road toll
systems.



CEN has developed the DSRC base standards, upon which the ETSI work is based;
Technical Committee TC 204 of the International Organization for Standardization
(ISO) is working on Intelligent Transport Systems, and its TC22 is working on in-car
equipment;
The ICT Standards Board has an Intelligent Transport Systems Steering Group.
5.5.3.2. DSRC as signal of opportunity to improve GNSS performance
From a general perspective, the availability of OBUs integrating GNSS and DSRC makes
possible the processing of DSRC signals to improve the performance of the GNSS receiver.
This started to be investigated in the recent years, because vehicular applications are among
the most demanding systems for accurate position information. Although most of vehicular
navigation systems can generally rely on satellite based positioning, other emerging systems
in the road domain may not use GNSS data only, due to their limited accuracy and
availability. Safety-related applications, such as collision avoidance or lane level guidance, are
some examples. Over the past decade, some innovative approaches have been presented to
enhance position accuracy within vehicular networks. Most of these are based on
communicating data among the nodes of a network: this concept is often referred to as
Cooperative Positioning (CP) [RD84].
A variety of modern CP techniques based on vehicular communication and RFID have been
proposed. Some of the most promising are based on algorithms that use the Doppler shift
between a the target node and its neighbours to estimate ranges [RD85]. The main motive
behind this choice is that the Doppler shift is considerably less distorted by channel fading
and multipath, which are dominant sources of errors that impact other techniques, like
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Received Signal Strength (RSS), Time Of Arrival (TOA) and Time Difference of Arrival (TDOA).
Another advantage of the method presented in [RD85] and [RD86] is that it does not require
synchronization between participating nodes. Although at the time of writing a small
percentage of vehicles is equipped with OBU integrating GNSS and DSRC, some experts
believe that vehicular communication platforms will be standard in the near future [RD87].
The use of DSRC signals to help GNSS in challenging environments allows for an
improvement on the horizontal accuracy. The results reported in [RD85] indicate a reduced
horizontal positioning error of about 50% respect to standalone GNSS receivers. However,
even if the integration of GNSS and DSRC is a technology to look at in the next years, several
open points remains before a real deployment of cooperative positioning with DSRC.
5.5.3.3. Technological Issues of combination of GNSS with DSRC
The use of GNSS combined with DSRC for road tolling can be considered a mature technology
and commercial OBUs are now available on the market, with a cost in the range of [120-200]
Euros per unit. These OBUs are thought to preserve the interoperability of different national
tolling systems and ease the migration from DSRC-based to GNSS-based charging schemes.
On the other hand, the processing of DSRC signals as signals of opportunity is a promising
technology, but with a lower TRL and with open issues to be solved. The most important are
summarized below.

The distance between the nodes (i.e.: vehicles equipped with DSRC tags) is assumed
to be estimated by some radio-ranging methods (e.g.: TOA, TDOA), but the
constraints and the limits imposed by the communication medium and by the mobile
environment corrupt the measurement accuracy and are not yet well acknowledged.

A more robust alternative to measure the distance between nodes is based on the
estimate of the Doppler shift between the nominal carrier frequency and the frequency
of the received signals. However, this method has not been assessed through an
intense on-field measurement campaign. Results are promising, but they have been
obtained in simulation, with doubtful assumptions of some parameters (i.e.: traffic
intensity, number of peers surrounding the targets, etc.) that could impact the final
performance. Results mainly refer to a highway scenario, whereas more challenging
environments should be considered.

CP relies on measurements from other vehicles, that must be equipped with OBUs,
integrating GNSS and DRSC. The higher number of vehicles, the better. Although
some experts believe that DSRC will be a standard in the next generation of vehicles,
at the time of writing the use of those OBUs is limited.
5.5.3.4. Perspectives toward the road sector
All the vehicles already equipped with an OBU integrating GNSS and DSRC - for the purposes
of road tolling – might exploit DSRC technology in supporting positioning authenticity (at
least at discrete locations within the coverage area of RSUs), being this a significant need for
PCAs. Moreover, assuming that [RD79]’s estimates will be confirmed, the effectiveness of
DSRC in supporting a more accurate positioning appears to be strongly limited by the poor
quality of the ranging measurements.
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Score
Justification
Availability
M
Assuming a sufficient numbers of vehicles
equipped with integrated GNSS+DSRC,
increased availability of the vehicle positions,
mainly in areas with poor visibility of the
GNSS satellites.
Accuracy
M
Assuming a sufficient number of vehicles,
integrated GNSS+DSRC enables Cooperative
Positioning techniques that can improve the
positioning performance of standalone GNSS
receivers. Early investigations demonstrated
an improvement up to 50% in some road
scenarios.
Performances depend by a number of
parameters (i.e.: number of peers, traffic
intensity, external environment, etc.)
Position authenticity
M
At least at location where DSRC is available
(e.g. within the coverage area of RSUs), it
can be used to validate the data coming from
GNSS sensors.
H (L)
H for GNSS+DSRC used for road tolling.
L for DSRC as a source of signals of
opportunity.
L (M/H)
L for GNSS+DSRC used for road tolling.
Products already on the market.
M/H for DSRC as a source of signals of
opportunity.
L/M
OBU cost in the range of [120-200] Euros per
unit
L
According to some estimates [RD79], DSCR
will be one of the most popular technologies
in the automotive sector in 2022. It is likely
to assume a further reduction of the current
costs.
M/H
The major constraint related to the use of
DSRC terminals as positioning modules
seems related to the limits imposed by the
communication medium. These do not allow
for accurate ranging measurements.
The poor availability of vehicles equipped
with DSRC-based positioning modules limit
CP techniques.
L
Base standards for DSRC terminals have
already been developed, at least for their
conventional use as RF tags
Performance
Maturity
Technological readiness
Time-to-market
Cost
Estimated cost in 2015 (current)
Estimated cost in 2025
Challenges
Technological challenges
Framework conditions
(i.e. regulation and standards)
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5.6. METHODS FOR PRECISE POSITIONING
Today the design limit for the accuracy of the GPS system for a “stand-alone” receiver can be
set to three to four meters, which is considered not entirely satisfactory by many users
[RD88].
GNSS accuracy is plagued by various types of errors linked to either the satellite system or
the physics of the electromagnetic propagation (e.g., refraction and attenuation through
troposphere and ionosphere). The sum of these errors can be easily measured at any point
on Earth, if the actual coordinates of a receiver are known accurately, and are likely to
remain valid in the vicinity of a given point over a small period of time [RD88].
Corrections provided by reference stations (differential augmentation) have greatly improved
the accuracy of positioning since the advent of GPS. Differential augmentation services
encompass various terrestrial networks of stations deployed on a national scale and
continent-wide systems developed by civil aviation authorities, which broadcast corrections
via geostationary satellites (i.e., WAAS, EGNOS, MSAS).
Networks of differential stations can help receivers to achieve accuracy levels from a meter
down to decimetre. But such accuracy levels are still considered insufficient for a number of
civilian applications, such as surveying, some Earth observation data, precise machine
guidance for off-shore exploration, mining vehicles, and automated farming [RD88]. Indeed,
historically precise positioning was associated with surveying and geodesy. It is nowadays
incorporated into production processes in mining, agriculture and construction. The main
application has been in machine guidance and machine automation which require high levels
of precision.
In order to achieve sub-decimetre level accuracy, receivers have to process carrier phase
measurement as well, to obtain information about their distance from the satellites (carrierphase ranging). In order to extract the correct range information from the carrier phase,
which is intrinsically ambiguous, two primary approaches have been employed over the last
15 years: Real-Time Kinematic (RTK) and Precise Point Positioning (PPP). RTK and PPP are
now state of the art for multiple frequency high end receivers used in agriculture, geodesy
and other professional application requiring cm level accuracy. Multiple frequency high end
receivers feature wide front end bandwidths and high sampling frequency, stable local
oscillators and algorithms for measurements selection.
The application of methods for precise positioning with low cost, mass-market receivers is
currently a research challenge.
In the following subsections we briefly review the main aspects of the two approaches and
highlight the technological challenges to be faced for their introduction in the market of the
ITS-enabling technologies.
5.6.1. PRECISE POINT POSITIONING (PPP)
The technical principle of Precise Point Positioning (PPP) is that measurement errors are
mitigated or removed from the position calculation using sophisticated modelling techniques
and correction products such as precise satellite orbit and clock corrections. GNSS corrections
are generated using data from a global reference network and they can be applied anywhere
on the Earth. By eliminating the need for a local reference stations, users can achieve
centimetre- or decimetre-level positioning in areas where it is not practical to use traditional
RTK techniques [RD89].
With respect to RTK, the major benefits offered by PPP can be summarized in [RD90]:


absence of direct and open-sky link with a local and nearby reference station;
global positioning approach, because its solutions are referred to a global reference
frame instead of a relative positioning to the local station;
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
reduction of labour and equipment cost, as well as operational logistics, since it
eliminates the dependency on local base station(s).
On the other hand, significant challenges remain ascribed to PPP:



it typically requires very long convergence times, due to the absence of a baseline that
helps in fixing the carrier ambiguities;
for very high precision (i.e.: <5 cm), the latency of the corrections matters, sometime
preventing the real-time;
in general, a more complicated processing at the receiver’s side is necessary with
respect to a solution which employs baselines (i.e., RTK)
5.6.1.1. PPP components
The major components of a PPP service can be identified in:
1. Correction products generated by the correction provider;
2. Delivery of the corrections to the user;
3. Computation of the position solution on-board a PPP-enabled GNSS receiver at the
user end.
The correction products are, at least, precise GNSS satellite orbits and clocks; they may be
also complemented with precise troposphere corrections [RD89] and ionosphere models
[RD91]. Correction products are today available from a number of public organization (e.g.,
the International GNSS Service, IGS, http://igscb.jpl.nasa.gov/, the Natural Resources
Canada, NRCan, http://www.geod.rncan.gc.ca/products-produits/ppp_dir_e.php, and Jet
Propulsion Laboratory, JPL, http://apps.gdgps.net/, which all provide a free-of-charge
service) and commercial providers (e.g., Fugro/Trimble, Veripos, NavCom, NovAtel), whose
services are on a subscription basis. The corrections are generated from large, most private,
receiver networks. A network consists of several tens, up to more than one hundred, of GPS
reference stations located around the globe, sometimes including also GLONASS receivers.
Major receiver networks are operated by IGS, JPL, StarFire (NavCom, John Deer), OmniStar
(Trimble) and TerraStar (Veripos).
The delivery of the corrections to the user may be realized through the Internet (as in the
case of IGS, which employs the Networked Transport of RTCM via Internet Protocol (NTRIP)
[RD92]) or via dedicated radio channels. The preferred solution for real-time processing is
broadcasting over the L-band satellite. This eliminates the need for a separate data link.
Indeed, by delivering corrections over satellite, user’s receivers do not need local base-station
infrastructure, cellular modem or Wi-Fi radio, greatly simplifying the user’s hardware
configuration. The Galileo Commercial Service (CS) introduces a new approach and foresees
the broadcasting of PPP data in the navigation message of the E6 signal [RD69]. On the other
hand, if the real-time is not a requirement, there are several ways to obtain coordinates in
PPP mode, using various scientific processing packet software (e.g., Bernese, Gipsy, WaPPP,
P3) or web-based online processing services (e.g., CSRS-PPP, GAPS, APPS, magicGNSS)
[RD93].
Commercial services promise near-real-time orbits and clock corrections, provided on a
subscription fee basis (on the order of 1000 Euros per half a year, per receiver). Free-ofcharge products are released with various latencies, which depend on the accuracy of the
computed correction. For example, the accuracy and latency of the IGS products is reported
in Table 5.1, extracted from http://igscb.jpl.nasa.gov/components/prods.html (accessed on
Feb. 2015).
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IGS Product Table [GPS Broadcast values included for comparison] -- updated for 2009!
Sample
Accuracy
Latency
Updates
Interval
GPS Satellite
Ephemerides/Satellite & Station
Clocks
orbits
~100 cm
Broadcast
real time
-daily
~5 ns RMS
Sat. clocks
~2.5 ns SDev
orbits
~5 cm
Ultra-Rapid
at 03, 09,
real time
15 min
~3 ns RMS
(predicted half)
15, 21 UTC
Sat. clocks
~1.5 ns SDev
orbits
~3 cm
Ultra-Rapid
at 03, 09,
3 - 9 hours
15 min
~150 ps RMS
(observed half)
15, 21 UTC
Sat. clocks
~50 ps SDev
orbits
~2.5 cm
15 min
at 17 UTC
Rapid
17 - 41 hours
Sat. & Stn.
~75 ps RMS
daily
5 min
clocks
~25 ps SDev
orbits
~2.5 cm
15 min
every
Final
12 - 18 days
Sat. & Stn.
~75 ps RMS
Sat.: 30s
Thursday
clocks
~20 ps SDev
Stn.: 5 min
GLONASS Satellite Ephemerides
every
Final
~3 cm
12 - 18 days
15 min
Thursday
Atmospheric Parameters
Final tropospheric zenith path
4 mm
< 4 weeks
weekly
2 hours
delay
Ultra-Rapid tropospheric zenith
6 mm
2-3 hours
every 3 hours
1 hour
path delay
2 hours;
Final ionospheric TEC grid
2-8 TECU
~11 days
weekly
5 deg (lon) x
2.5 deg (lat)
2 hours;
Rapid ionospheric TEC grid
2-9 TECU
<24 hours
daily
5 deg (lon) x
2.5 deg (lat)
Table 5.1: IGS product table.
The GDGPS System of JPL provides a global real-time map of ionospheric electron content,
currently updated every 5 minutes.
The computation of the position solution at the receiver side entails several requirements
posed on the user’s receiver. First, the receiver must be enabled to retrieve all the necessary
corrections, either through the Internet or via its radio channel. Second, it must carry a set of
advanced algorithm to provide highly stable measurements and to adequately process the
corrections to achieve positioning accuracies on the order of a few decimetres or centimetres.
The final achievable positioning accuracy depends in part on the quality of the corrections
and, mostly, on the quality of the user’s equipment [RD90]. Of course, highly degraded
environments (e.g., multipath, interference and non-line-of-sight propagation) dramatically
worsen the achievable accuracy.
5.6.1.2. Error Mitigation in a PPP-enabled receiver
Advancements in GNSS positioning have, to a large degree, been due to progresses made in
the modelling and mitigation of the various error sources that corrupt the measured ranges.
Error mitigation approaches in a high-precision receiver can essentially be divided into three
categories:
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1. Signal combinations
2. Error models
3. Externally-provided information
On top of this, the receiver must resort to carrier-phase ranging in order to exploit the cm-tomm precision of the phase observables [RD94].
PPP can be considered at the apex of GNSS error mitigation and uses all of the approaches
listed above.
For instance, to remove the effects of the ionosphere, PPP-enabled receivers typically use
combinations of signals on different frequencies [RD89], [RD69], [RD90].
Troposphere errors are reduced by advanced troposphere delay models, and then further
mitigated by zenith delay dynamic models [RD89].
PPP data providers supply corrections that remove the effects of satellite clock and orbit
errors. There is some latency between the calculation of the satellite positions and clocks on
the provider side and their use on the client side. For this reason this latency must be
accommodated by the PPP filter [RD89].
PPP also requires a number of unconventional corrections to mitigate systematic effects that
could cause centimetre variations in the code and phase observations [RD90]. Phase wind-up
correction, satellite antenna offset, and site-displacement effects due to solid Earth tide and
ocean loading are some examples [RD95]. These corrections are not considered for standard
point positioning, where the accuracy remains above the meter level, nor in double-difference
RTK positioning, where these effects cancel out thanks to the relative positioning.
The net effect of the PPP error mitigation is to reduce the GNSS carrier-phase measurement
precision to the amount of the remaining unmitigated errors. With a high-quality PPP
correction feed, this error can be reduced to only a few centimetres. However, the wellknown problem of the ambiguity in the carrier-phase measurements still remains [RD89]. In
the absence of any baseline (i.e., any known reference station nearby the user’s receiver, as
in the case of RTK), ambiguity fixing becomes very challenging, because the search space for
the correct integer fixing is huge, and therefore it takes time to resolve
[RD91][RD96][RD97]. This time is the so-called convergence period.
One way for improving convergence is to improve the geometry of the solution by adding
additional satellites. This is the reason why multi-constellation receivers are preferred also for
high-precision processing [RD89], [RD98].
Reference [RD89] reports convergence times on the order of 30 minutes for an horizontal
accuracy of 0.2 meters (95%), using a top-grade dual-frequency receiver with real-time
corrections. Similarly, [RD91] reports for the same accuracy a convergence time less than 20
minutes using a dual-frequency with non-real-time corrections (“Final” IGS orbits and clock
corrections, with up to 18 days delay).
Reference [RD91] demonstrate how single-frequency PPP can achieve much faster
convergence time thanks to the use of ionospheric corrections read from and external source
(e.g., the Global Ionosphere Map from IGS) to correct for ionospheric delays. This way, the
inter-frequency differences to remove the ionospheric effects are not needed anymore, which
are known to be very noisy and amplify multipath and receiver measurements. Since the
noise is not amplified in the undifferenced single-frequency measurements, convergence time
is much faster [RD91]. Of course, the price is an accuracy which cannot be reduced below
some decimetre.
5.6.2. REAL TIME KINEMATIC (RTK)
Real Time Kinematic (RTK) is the state of the art of some professional applications based on
high end GNSS receivers employed in open sky conditions (e.g.: precision agriculture for the
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control of seeding and fertilizers spreading, geodesy and surveying). These applications
require centimetre level accuracy for moderated (or static) users’ dynamics.
As done for the PPP, this section recalls the fundamentals of the RTK and presents some
results of early investigations on the use of RTK for mass-market applications. Indeed, RTK
algorithms combined with single frequency, low cost receivers represent a research topic,
that arise interest in the road applications domain.
5.6.2.1. RTK fundamentals
Errors due to the signal propagation in the atmosphere, not precise satellite orbits and
satellite clocks offsets are correlated in space and time. A differential system takes advantage
from the fact that two receivers with a certain degree of proximity are affected by common
errors, namely the errors with a strong spatial correlation, that can be mitigated. By simply
differencing measurements from two synchronized receivers, spatially correlated errors are
eliminated or reduced, provided that the baseline (i.e.: the geometric distance between the
two receivers) is within a few kilometres. From the point of view of the implementation, two
approaches are possible:
1. the error experienced by the base (i.e.: a static receiver in a known position) is
estimated and sent to the rover (i.e.: the user’s receiver) as a correction;
2. the measurements of both receivers are combined together (i.e.: through single or
double differences), achieving the so called relative positioning between the two.
The second is the case adopted by the RTK positioning, which typically relies on double
differences between pseudoranges estimated through carrier-based measurements. The
result of such differences is used to estimate the baseline, that in turn is summed to the
position of the base (which is known and geo-referenced) to get the user’s position.
Models for carrier-based pseudorange measurements can be found in many books on GNSS
(e.g.: [RD97][RD99]). Compared to code phase measurements, carrier phase is more precise
but ambiguous. In fact, carrier phase measurements (in cycles) can be expressed similarly to
the code phase measurements with the addition of the ambiguity term. The necessary
hypothesis is that the receiver’s Phase Lock Loops (PLLs) stay locked to the incoming signal
carrier phase and keeps estimating its variations over time without interruptions. Thus, the
main task for RTK methods is the resolution of such ambiguity, which has three fundamental
characteristics:

it is an additional unknown for the position determination;

it is different for each satellite;

it is a constant, supposing that a continuous lock of the carrier phase tracking loop is
maintained, which is likely in open sky condition for a medium-high satellite elevation,
rather difficult in other types of environments.
Once the carrier is locked by the receiver, it is able to keep trace of the change of the
distance in term of phase, counting the fractional number of cycles and accumulating them
over time (the so-called accumulated Doppler). These measured phases are added to the
unknown ambiguity (that is an integer if the double difference approach is used) and become
valid for ranging (i.e. satellite-user distance) and for the computation of the baseline. The
computation of this ambiguity requires significant computational effort, because the
algorithms seek a set of suitable values in a finite search space.
Then, once the integer ambiguity is solved for integers, the receiver switches in the so called
“on-the-fly” mode and the user’s position can achieve centimetre-level accuracy. From this
moment on the user’s receiver has just to monitor the quality of the estimated phase to
detect the presence of cycle slips that corrupt the measurement, but the ambiguity resolution
has not to be performed anymore, provided that the phase tracking is not interrupted. This
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process keeps valid until a signal blockage forces the PLL to lose the lock. If so, the process
needs to be restarted to fix a new value of the ambiguity.
5.6.2.2. Performance of RTK with high end receivers
Today RTK allows for improving the accuracy of the estimated position down to centimetrelevel. Typical values of the mean error on the North, East and vertical coordinates are 1.5
centimetres, provided that:






the baseline is within 10 Km;
the base and rover use dual or multi-frequency receivers that perform carrier based
measurements;
the rover is static;
the antennas have stable phase centre
the receivers feature multipath mitigation capabilities;
there is a good visibility of the satellites.
Table 5.2 reports in the third column a typical user equivalent error budget for a carrierbased, dual frequency differential GPS, considers a baseline of 10 km 15. The table also reports
the error budgets associated to other types of signal processing for comparison 16.
One Sigma error [m]
Error source
Code-based, dual
frequency standalone
GPS
Code-based, single
frequency, differential
GPS (baseline 50 km)
Carrier-based, dual
frequency, differential
GPS (baseline 10 km)
Ephemeris
2.1
-
-
Satellite clock
2.1
-
-
Ionosphere
1.2
0.4
0.006
Troposphere
0.7
0.2
0.007
Multipath
1.4
1.4
0.03
Receiver Noise
0.5
0.5
0.01
Total rms
3.6
1.6
0.034
Table 5.2: Typical error budgets for different types of signal processing.
5.6.2.3. First results of RTK with low cost receivers
The results of the tests in [RD100] demonstrated that the performance of standalone GNSS
receivers with default settings were clearly above the meter level (i.e.: Horizontal positioning
error in the range [1.3 – 3.5] m) driving on a beltway with far buildings surrounding the
antenna. The error reached dozens of meters in the most constrained environments. This is
true either with low cost receivers or with geodetic-grade equipment. These results underline
the difficulty in performing satellite-based navigation in the urban environments.
Second, the direct application of RTK algorithms on top of carrier phase measurements at the
output of the low cost receiver (combined with a patch antenna) leads to unreliable ambiguity
fixing. As explained and well described with appropriated figures in [RD100], the ambiguity
15
16
http://gpspp.sakura.ne.jp/paper2005/isgps2008_paper_ttaka.pdf.
http://www.eduobservatory.org/gps/gps_accuracy.html][http://www.gps.gov/cgsic/international/2009/stockholm/emar
dsson.pdf
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fixing is unreliable 56% of the time on the beltway and up to 80% of the time in Toulouse
downtown. In these cases, the error on the position often exceeded 10 meters. Therefore, the
computation of a reliable integer ambiguity is a key point and specific algorithms must be
used to weight carrier phase and Doppler measurements, carefully excluding low quality data
and detect cycle slips. The risk is to have a RTK receiver with a high rate of incorrect fixes,
that is not usable.
[RD100] demonstrated that there are still quite a lot of challenges to reliably use precise
positioning in difficult environment. The major challenge seems the availability of carrier
phase measurements, that remain fragile, whereas the code pseudorange measurements,
used as references for fixing carrier phase ambiguities, can still be affected by strong errors
due to multipath or Non-Line-of-Sight (NLOS) tracking. However, the same paper proposed a
RTK solution based on Kalman filtering using code, phase and Doppler measurements, a
careful cycle slip detector and corrector, and a heavy measurement selection. It was
based on a low cost platform (i.e.: < 100 $) with a GPS/GLONASS single frequency receiver
and a patch antenna.
Table 5.3 reports same of the values presented in [RD100].
Filter method
Test #1
Test #2
Urban – HPE
Beltway – HPE
(68th percentile)
(68th percentile)
Baseline RTK filer
2.56
1.67
Advanced filer, with improved
measurement section
1.60
0.13
Baseline RTK filter
2.19
1.41
Advanced filer, with improved
measurement section
1.52
0.08
Table 5.3: Horizontal Position Error reported in [RD100].
These results show that:

In a “semi-urban” environment, such as the beltway, it was possible to solve the
integer ambiguity and compute precise positions for about 70 % of the time, that in
turn resulted into a positioning error lower than 10 cm.

In urban, it was possible to solve the integer ambiguity and compute precise positions
only for less than 20 % of the time. However, the proposed algorithms, designed to
carefully select/weight measurements and detect multipath, improved the positioning
accuracy, whose 68th percentile was about 1.5 m.
5.6.3. TECHNOLOGICAL ISSUES FOR PRECISE POSITIONING FOR ROAD USERS
This section discusses some technological issues to overcome before applying precise
positioning in road. They arise from two facts: first, the state-of-the-art architectures are
expensive in terms of equipment and access to the service; second, they cannot be directly
applied to any road scenario, because the non-open-sky and highly variable visibility typical
of the urban situation prevents in practice the use of technologies expressly developed for
open-sky operations. These reasons pushes the current research to look for affordable
solutions able to overcome the current barriers mentioned above. The challenges faced by the
current research are discussed hereafter.
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First, the trade-off between cost and performance: so far, PPP has been a technology for
professional applications, which require the highest possible accuracy and are available for
paying for it. For this reason, dual-constellation and dual-frequency receivers with
professional antennas have been the common, though expensive, choice for the employed
equipment. Nonetheless, PPP with single-frequency receivers has received increasing
attention because of the opportunity it can offer of reducing equipment costs (as well as
performance) [RD101][RD102][RD103][RD104]. Indeed recently, service providers have
started widening their offer with cheaper single-frequency PPP equipment with medium-level
accuracy, for less-demanding applications.
Second, the development of a suitable user equipment. The equipment quality is indeed
responsible for the ultimate performance of the positioning estimation, besides any external
information. The following aspects play a role in the quality (and cost) of the equipment:
1. It has been demonstrated that the quality of the antenna matters [RD98]
[RD105][RD106]: in the case of a low-cost antenna, there is no control on the
variation of phase centre of the antenna, which biases carrier-phase measurements.
In such a case, ambiguity resolution is more difficult and takes longer time.
Furthermore, extremely-low-cost antennas for personal mobile devices (e.g.,
smartphones) cannot suppress multipath and experience quite irregular gain patterns,
even worsen the quality of the received signal.
2. The quality of the range measurements produced by the receiver should be good
enough to employ carrier phase-only ranging techniques and exploit their fine
resolution. It means that tracking loops must be designed and controlled for high
stability, low cycle slip probability and tracking robustness against multipath and
signal fluctuations [RD98], that are likely in urban contexts
3. In the case of real-time applications based on PPP, the receiver must be able to
retrieve (i.e., downloading from the Internet or receiving an ad-hoc radio message),
decode and correctly apply the PPP corrections, including those specifically needed for
non-differential processing. Furthermore, it must be smart enough to apply adequate
signal selection strategies to exclude from the PVT solution all degraded satellite
signals without penalizing too much the visible constellation [RD98]. On the other
hand, in the case of non-real-time processing these operations are demanded to a
remote server which executes post-processing, typically at the end of the mission. In
such case, the receiver must collect and store (or send) its measurements to the
remote server, which then computes the precise positions covered by the receiver.
Furthermore, the type of the environment surrounding the receiver directly impact the
performance of the methods used for precise positioning. In some cases, the environment
can pose a barrier difficult to overcome. In fact, as already said, carrier-phase processing
is based on two mandatory assumptions: (i) no cycle slips occur inside the phase
synchronizer in the observed time interval, and (ii) there is no loss of lock of the phase
synchronizer to the received carrier signal (i.e., the ambiguity term does not change). Of
course, if these requirements deserve the utmost care in open-sky conditions, they become
really hard in degraded propagation conditions, i.e., in the case of frequent signal blockages,
signal fading, non-line-of-sight and multipath propagation, as it is common in urban
environments.
5.6.4. PERSPECTIVES TOWARD THE ROAD SECTOR
Recently, precise positioning methods have been investigated on “light professional”
receivers, devices which may admit even decimetre-level accuracy for applications that do
not need the accuracy provided by survey-grade receivers, but that require better
performance with respect to consumer grade products, with a price pressure much less
extreme than consumer products [RD107]. Following [RD107], such kind of applications
might be for example agriculture and trains. However, we believe that also other applications
in the road domain (e.g.: precise navigation of vehicles for road maintenance) have the
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potential for being classified as “light professional” as per the given definition. We exclude the
use of light professional receivers for personal leisure or navigation where the current
accuracy is normally perceived as satisfactory enough not to spend more money for
improving it [RD107].
The major barrier for the use of carrier-phase measurements is the requirement for
continuous and precise phase tracking. This can be done in receivers which implement
continuous carrier tracking loops (i.e., some smartphone-grade receivers must be excluded
[RD70][RD71]), techniques for multipath mitigation, and cycle slip detection and correction.
Furthermore, dense urban environments could really deteriorate the achievable performance,
but the advent of new constellations, and therefore the availability of more satellites in view,
will improve the continuity, as recently demonstrated by [RD98][RD93][RD108].
Taking these considerations in mind, it is possible to argue that the penetration of precise
positioning in the road sector is on its way, although with a likely different flavour with
respect to the classic approaches popular in geodesy, survey and precise machine guidance.
The principal aspects that could characterize this new market entry are arguable as follows:

First, it can be expected that road-domain applications require “several-decimetre”level precision instead of sub-centimetre-level. This would relax the accuracy
requirement with an immediate impact on the equipment complexity: single-frequency
receivers could emerge in the market, enabled with a medium-complexity PPP signal
processing software and equipped with high-level (though not top-level) antennas. A
relaxed requirement in terms of positioning accuracy would also allow dramatically
reducing the convergence time, one of the major drawbacks of PPP.

The cost for such a “low-complexity” PPP receivers can be expected to be more
affordable than current PPP-enabled devices available today on the market. From
some perspectives, this fact could help the market penetration [RD109]; on the other
side, a cost reduction down to a “mass-market” level cannot be reasonably expected
in the short/medium term. Therefore, the first PPP penetration may be expected in
professional applications related to the road domain (e.g., precise navigation of
vehicles for road maintenance), where a clear economic revenue for the investor
justifies the sustained costs.

The main advantage of PPP with respect to RTK is that it provides a global and
absolute positioning and timing service without the need of nearby reference stations.
Long converge time is a drawback mainly if centimetre levels accuracy is targeted.
Observing current trends and advanced of the PPP technology in the last decade, we
believe that PPP services for road applications will become common in the next future.

The poor performance of algorithms for carrier-based ranging in degraded
environments is still an open issue. Although it can be mitigated with multiconstellation coupled with ad-hoc processing, this approach is not at the state-of-the
art and likely deserves further studies and validations. The development of receiverside techniques able to mitigate such problems is an open research topic, as witnessed
for example by [RD98]. For these reasons, the earliest applications should be likely
expected limited to open-sky (or mostly-open-sky) situations, such as motorways in
rural environments, airports, large parking areas, etc.
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Score
Justification
H
Methods for precise positioning, either PPP and RTK, can
be applied on top of measurements taken with low cost
GNSS receivers, drastically increasing the positioning
accuracy that can reach centimetres-level. At the time
of writing this is a hot research topic, raising interest in
the road domain.
Availability
L/M
A major drawback of carrier-phase positioning in road is
the availability of carrier phase measurements, that
remain fragile in urban contexts. In environments
characterized by narrow streets with high building
surrounding, it is still not possible to fix the carrier
phase ambiguity most of the time. It is expected a
tremendous benefits with new constellations.
Continuity
L
In urban, obstacles and buildings can obscure the LOS
signals and break the carrier phase measurements, that
in turn result into a loss of continuity of carrier phase
positioning.
L
Results obtained processing data sets collected in a real
environment, but at a proof of concept level.
M/H
Since the technological readiness associated to PPP and
RTK with mass market receivers is considered low, the
time-to-market is supposed long. However, the
availability of new satellites from new constellations will
encourage the research on this topic, as it will increase
the possibility to use carrier phase measurements for
precise positioning also in challenging environments.
This is valid either for PPP and RTK.
L/M
N.A
L/M
The major barrier of this application seems the poor
availability of carrier phase measurements and the need
of rather complex algorithms for the selection and
filtering. However, it is reasonable to think that the
costs associated to this technology, once the algorithms
will be assessed, will be negligible with respect to
current solutions.
M/H
The availability of carrier phase measurements is
essential for precise positioning. The road environment
corrupts such measurements, so that it is necessary to
filter and carefully select measurements before
performing RTK and PPP. Such filtering and selection
processing is rather complex and is associated to a high
computational burden. Alternatively, more robust carrier
phase tracking loops should be designed.
The quality of the antenna, with a stable phase centre,
is also required. Most of the antennas used today in
road application do not feature such stability and cannot
be used when combined with precise positioning
methods.
Performance
Accuracy
Maturity
Technological readiness
Time-to-market
Cost
Estimated
(current)
cost
in
2015
Estimated cost in 2025
Challenges
Technological challenges
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5.7. ADVANCED DIGITAL MAP MATCHING
In strongly constrained environments, like city centres, the propagation phenomena in the
surrounding of the antenna, more precisely diffraction and multipath, are responsible for
severe errors on the raw observables (pseudo-ranges and Doppler measurements) that are
measured by the receivers. The most severe deviations (up to several tens of meters) may
occur in case the reflected path is the only tracked, whereas the direct one is blocked. Such
signals are called Non-Line-Of-Sight (NLOS) signals.
A standard low-cost receiver, with no particular knowledge of the environment, acts blindly
when computing the navigation solution from the raw observables. It may filter the
observables output by the satellite signal tracking process according to the signal-to-noise
ratio (SNR) or possibly according to the elevation angle. But mainly, since it gives greater
place to continuity and availability compared to accuracy, it will use most of the
measurements even if the latter are strongly corrupted by propagation phenomena.
When some a priori knowledge of the environment is embedded in the receiver, for instance
under the form of a digital map with building height information, the processing (and
resulting accuracy) of the navigation solution can be significantly improved because the
conditions of reception of each signal can be characterized. The level of characterization
depends on the accuracy and completeness of the map, and on the computation power one
can use for this purpose [RD112].17
Digital maps with 3D data proved to make it possible the determination of NLOS satellites in
real time and obtain significant benefit in terms of navigation accuracy. However, such data
are difficult to handle with Geographical Information System (GIS) embedded software in real
time.
State-of-the art map matching is based on the principle of computing the distance between
the estimated vehicle location and the nearest road segment of a digital map [RD113], in
which roads are represented by one or two polylines (depending on whether lanes with
opposite driving directions are physically separated), i.e., a series of nodes and shape points,
connected by segments. A review of the current map-matching algorithms for transport
applications can be found in [RD32]. More recent works [RD114] [RD115] [RD116] propose
to solve the positioning problem with the digital map constraints using a particle filtering in
which the particles leaving the road are eliminated.
In the last years, researchers have addressed the use of 3D models of the environment to
analyze the conditions of reception and mitigate multipath phenomena. 3D map data were
introduced in the positioning problem by [RD117], where a detailed LOS-NLOS visibility
boundary is generated by ray-tracing from (and around) the a priori receiver location. The
underlying idea is to separate the satellites which are in direct visibility from the ones which
are hidden or in indirect visibility. Several other similar approaches have been proposed, for
instance [RD118] [RD119] [RD120] [RD121], in which the integration with motion sensors
and/or road map constraints are used to improve the final accuracy performance, without
affecting too much availability. In addition to the aforementioned techniques, “shadow
masking” was introduced by [RD122] and seems to bring additional interesting information.
Last, [RD123] models a path delay for NLOS, in order to compute and apply a corresponding
range correction directly.
17
A different a-priori knowledge of the surrounding environment can be obtained through the use of
visual sensors, as discussed in Section 5.3.1.3.
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5.7.1. TECHNOLOGICAL ISSUES FOR ADVANCED DIGITAL MAP MATCHING
At the time of writing, the use of advanced 3D digital maps has been investigated mainly in
research works and demonstrated in some very heterogeneous field tests. For this reason
the maturity of the advanced digital map technology to improve GNSS accuracy should be
considered still low.
The main issues associated to the described approach are related to the availability of the
advanced digital maps (construction, installation and processing on board of the receiver
device, update, data format) and to the risk of reducing the availability of the positioning
service although improving the accuracy. These issues are well argued in the promising work
[RD112]:

Advanced digital maps: The additional data included in a digital 3D map are difficult to
handle in real time with GIS embedded software, due to the high volume of data to be
stored and the moderated processing capabilities of the in vehicle embedded systems.
Therefore the information contained in the map should be designed as simple as
possible, so that it matches the requirements of usual embedded and navigable maps.
This should actually be registered as a set of attributes applicable to the standard
polyline structure of 2D digital maps [RD112].

The construction of such maps is another issue: they are available, not for free, for
some city centres, but it seems that no standardized data formats for this kind of
applications have been agreed. Therefore applications must be tailored to proprietary
maps and formats, possibly not optimized for the purpose of this application.
Furthermore, the advantage of such methods is strictly related to the update of the
map, meaning that it reduces with non-updated maps.

Availability of the accurate fixing. The exclusion from the navigation filter of the
satellites in NLOS increases the position accuracy, but is likely it poses a problem of
availability. Indeed, without additional sensors (i.e.: inertial or motion), the number of
LOS pseudoranges could be often less than four in urban scenarios, preventing the
position fix computation. The work [RD112] experimentally demonstrates that the
median of the positioning error can improve from 40% to 70% using LOS satellites,
with respect to a standard solution that takes all the tracked satellites. However, with
the LOS only the availability of the final position has dramatically decreased, since a
positioning solution has been computed for only 70%-80% of the total number of
epochs, against almost 100% with all satellites. With a mixed solution, where NLOS
satellites data are progressively re-introduced in order to avoid unavailability, the
number of position fixes raises again to almost 100% at the cost of a reduction of the
accuracy improvement to 30%-65% [RD112].
5.7.2. PERSPECTIVE TOWARD THE ROAD SECTOR
Leveraging on the progress of the technology for data computing, 3D maps will be a mean to
improve the position accuracy provided by standalone GNSS receivers. Multi constellations
are an advantage for the integration of GNSS with 3D maps, because they smooth the risk of
reduced availability after the filtering of NLOS measurements. Despite these advantages, the
current level of maturity of this technology seems not ready to support new products in the
short/medium term.
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Score
Justification
Accuracy
M
Improved accuracy thanks to NLOS error
mitigation. Expected still at the meter level
(although a clear and stable characterization
is not available yet)
Associated risk of reducing the availability of
the positioning service in urban scenarios
when measurements are filtered and LOS
only measurements are used
Position Integrity
L
Risk of reducing the availability of the
positioning service in urban scenarios when
LOS only measurements are used
Technological readiness
L
Several proposals, but all at the level of
research work (with demos)
Time-to-market
H
Barriers can be seen in the difficulty in
handling 3D maps in real time and in dealing
with non-standardized data formats.
Furthermore, the availability of 3D maps is
not worldwide guaranteed
Estimated cost in 2015 (current)
N.A.
N.A.
Estimated cost in 2025
N.A.
N.A.
M
Availability and handling of 3D maps
Maturity of the proposed approaches
Performance
Maturity
Cost
Challenges
Technological challenges
Framework conditions
Lack of standardized map data format
tailored for the specific application
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6. CONCLUSIONS AND RECOMMENDATIONS
A revised version of Table 4.4 in Section 4.3 is reported at Page 77 (Table 6.1), listing the
potential technological enablers able to achieve the expected positioning performance
features. Such a revised table aims at summarizing the major results, pointing out some final
considerations.
The added value of Table 6.1 is represented by an “overall evaluation score” assigned to each
technological enabler with respect to a specific positioning performance feature. This is the
outcome of the investigation of each technology, as a synoptic view of the tables at the end
of each subsection of Section 5.
In particular, the overall evaluation score in Table 6.1 is simply provided with a coloured tick,
being:

green  a technology that allows to effectively support the specific performance feature,
without significant technological barriers to be overcome towards a short-term full
readiness18;

yellow  a technology that either is not able to provide a full achievement of the
expected performance, or its short-term adoption is prevented by some barriers (e.g. a
low TRL or a significant cost);

red  a technology that is not considered able to achieve the expected positioning
performance.
Looking at the content of Table 6.1, the following considerations can be derived:

A number of technologies supports the “position accuracy”, even if with different level
of effectiveness and maturity. Among them, the multi-frequency receivers and the
GNSS/IMU tight integration appear to be the most ready-to-market, but with potential
accuracy not less than a few meters. An effective support to an higher accuracy is allowed
by precise positioning techniques such as PPP and RTK with “light professional” GNSS
receivers: however, even if a topic raising interest in the road domain, the precise
positioning techniques are still a research topic.

The “position authenticity” appears to be effectively supported by a number of different
and complementary technologies. If some of such technologies are somehow related to
GNSS (e.g. the signals authentication ensured by Galileo through the use of multifrequency receivers), it is worth noting the role of non-GNSS solutions, i.e. the integration
with SoO, in cross-checking the position authenticity. However, these non-GNSS solutions
are not originally conceived as authentication methods, then some efforts shall be
devoted to make them effective (for instance, the development of proper algorithms
aiming at check the position authenticity leveraging the SoO features).

Also considering the “positioning authenticity”, it is worth mentioning the different stage
at which the different available technologies are effective: the GNSS signals
authentication is the only method that can ensure a priori authenticity of the position,
while all the other solutions can only support a posteriori check of the authenticity then
enabling a detection function.

Concerning the “robustness to interference”, a quite limited set of technologies seems
to be available among those analysed. While the use of multi-frequency receivers,
exploiting their inherent frequency diversity, and the implementation of a GNSS/IMU tight
integration can be considered ready for the market, the integration with SoO (e.g. digital
TV and cellular network signals) is subject to a joint regulation with standardization
18
It is worth mentioning that, even if each technology has been also analysed in the perspectives of the
road sector and the outcomes of this analysis is considered in formulating the overall evaluation score,
“readiness” has to be intended here from a purely technological point of view. As a matter of fact, the
adoption in the road domain of a specific technology may be driven by other opportunities.
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bodies. In addition, the proper algorithms to exploit the benefits of such an integration for
interference mitigation are not state of the art.

Any time GNSS receivers are combined with inertial sensors or augmented with terrestrial
navigation technologies (i.e.: DVB-T and cellular networks), there is an inherent
improvement of the availability. This because in case of short/medium GNSS signal
outages, the PVT data can be provided by non-GNSS means.

Focusing on the rows of the table, it can be noted that the technologies supporting the
“position accuracy” (e.g. the GNSS/IMU tight integration) are also effective in terms of
“position integrity”. In fact, more accurate position estimation may result in a better
protection level, then broadly speaking, increasing the positioning integrity.
In conclusions:

Even if not yet state-of-the-art in the GNSS-based positioning in the road domain, the
implementation of a GNSS/IMU tight integration represents a cost-effective solution able
to support most of the performance features (e.g. accuracy, authentication, integrity),
especially if complemented with multi-frequency receivers;

The precise positioning techniques (i.e. PPP and RTK) on top of measurements taken with
mass market GNSS receivers come with a quite low TRL today, but the availability of new
satellites from new constellations (e.g. Galileo) will encourage the research on this topic,
as it will increase the possibility to use carrier phase measurements also in challenging
environments like urban.

When available (e.g. vehicles equipped with a dedicated OBU as for the DSRC), the SoO
may represent a good solution in complementing EGNSS mostly for the purposes of
position authentication, since they are non-GNSS technologies. Their contribution for
other features is not yet mature, but DSRC – as soon as some limitations in the quality of
measures will be overcome - appears a promising technology to look. In theory, DSRCbased positioning modules enable cooperative positioning, that in turn may increase the
position accuracy. This is a potentiality also fostered by the foreseen significant
penetration of such technology, now driven by road rolling needs.

The added value provided by the current non-GNSS sensors (low cost, but low
performance) is very limited. An effective support to the positioning performance,
accuracy at first, is expected from high-end sensors, but comes with medium-high cost.
For this reason, such sensors are expected in the short/medium term for specific
demanding applications such as the autonomous driving.
Technical Analysis of New Paradigms Increasing
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Sect. 5.3.1

Tight integration with IMU
Sect. 5.3.2

Civilian GNSS signals authentication
Sect. 5.4.2

GNSS spoofing countermeasures
Sect. 5.4.3

Integration with SoO – DVB-T
Sect. 5.5.1
Integration with SoO – Cellular networks
Sect. 5.5.2
Integration with SoO – DSRC
Sect. 5.5.3

Precise Positioning – PPP
Sect. 5.6.1



Precise Positioning – RTK
Sect. 5.6.2



Advanced digital map matching
Sect. 5.7


Position
integrity
Combination of GNSS receivers with vision sensors

Availability

GNSS
sensitivity

Robustness to
interference
Sect. 5.2
Position
authenticity
Time accuracy
Multi-frequency receivers
TECHNOLOGICAL ENABLERS
TTFF
Position
accuracy
PERFORMANCE FEATURES
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



Continuity
Date:
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








Table 6.1: Overall evaluation of the identified technological enablers for the performance features.







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