Indoor localization method comparison Fingerprinting and

Indoor localization method comparison Fingerprinting and
Indoor localization method comparison: Fingerprinting and
Trilateration algorithm
Ting Wei and Scott Bell
Geography and Planning, University of Saskatchewan, ting.wei@usask.ca
Geography and Planning, University of Saskatchewan, smb332@CAMPUS.USASK.CA
Abstract
Enhanced Positioning Systems (EPS) are able to supplement Global Positioning Systems (GPS)
in indoor environments where GPS cannot work because of disrupted or weak signals. Most EPS
are Wifi-based because Wifi is a common technology available in many indoor environments
and is deployed in cost effective manner. Fingerprinting and Trilateration are the two general
methods used for calculating position with Wifi-based EPS. This paper will briefly introduce
these two methods, summarize their common ground and differences, and compare the
strengths and weaknesses of each.
Background and Relevance
As GPS becomes a routine tool for navigation and wayfinding more and more mobile
handled devices (smartphones and PDAs) are integrating GPS. However, an important
and well-known limitation of GPS is that it cannot work inside buildings because of the
weak signal’s inability to penetrate building material. EPS is an important supplement
for GPS in indoor environments where GPS cannot work. Most EPS are Wifi-based
because Wifi is a common and accessible technology that provides the basic information
necessary for indoor positioning without requiring additional hardware. In the case of
Wifi-based indoor localization, the fingerprinting method based on Wifi singnal
strength observations is often employed (Mok & Retscher, 2007). An alternate method
is the trilateration algorithm which is also implemented in GPS; trilateration uses
distance to surrounding Access Points which, in the case of Wifi routers, is derived from
signal strength values.
Methods Comparison
Fingerprinting can be generally divided into two phases: an offline phase and an online
phase. The offline phase involves building the signal strength database and creating the
signal strength map. After creating an accurate database of Access Point (AP) locations,
reference points are chosen. Evenly spreading these reference points in the
experimental area improves the accuracy and reliability of the locations derived from
fingerprinting. The received signal strength from every visible AP is included in the
database for each reference point. After measuring the received signal strength from
each visible AP, the mean value of the signal strength and the distribution of signal
strength of each reference point will be calculated and stored in the database. During the
online phase, both deterministic and probabilistic methods can be employed as a
positioning algorithm (Zhou, 2006). The former chooses the reference point in the
database whose signal strength has the minimum difference from the received signal
strength of the device as the most probable location; the latter chooses the most likely
location of the device in database as the most probable location.
The trilateration algorithm does not need an offline phase like fingerprinting. However,
trilateration still needs an accurate AP location database, including accurate Access
Point coordinates and the unique Media Access Control (MAC) address for each AP.
During active measurement, after calculating average signal strength for each visible AP,
the system uses this value as an approximation for distance to trilaterate the device’s
location. It is of considerable importance that the general relationship between signal
strength and distance may vary from different networks of APs, so it is practical and
necessary to recalculate the general relationship when the network of Access Points
change. This also suggests that trilateration benefits from the use of a common or small
set of AP models. The common ground of the two methods is the need for an accurate
database of AP locations and the dense and consistent wireless signal.
The differences between the two methods lead to some strengths and weaknesses. From
a cost perspective, compared with trilateration algorithm, using fingerprinting
consumes more time and labor during the collection of signal strength data and a huge
volume of data needs to be stored as fingerprinting depends on a pre-existing signal
strength database for all reference points. For a reasonably sized building, the offline
phase of fingerprinting could take over 100 hours (Bahl & Padmanabhan, 2000).
Positional accuracy with a fingerprinting algorithm is positively associated with the
density of reference points in the database. The trilateration technique, on the other
hand, includes as a database creation process but without collecting signal strength data.
From the perspective of adaptability, the trilateration technique performs better than
fingerprinting. When a router is installed or removed in the environment, the
trilateration technique only needs to add that new record to the database (with its
accurate location and MAC address) or delete the existing record in the database; the
fingerprinting technique, on the other hand, signal strength data needs to be recollected
for every reference point within range of that new or removed router.
From the perspective of signal strength, fingerprinting takes into account the
attenuation because the actual signal strength at each reference point is collected (which
integrates the presence of obstructions between device and routers). Trilateration, on
the other hand, collects signal strength values in real time and converts them to
distances, taking no account of possible obstructions. The distance used for trilateration
will be same for common received signal strength whether a signal is passing through
walls or travelling through an obstruction-free space. To reduce this effect, a correction
factor should be added to revise the average of the signal strengths for the non-line-ofsight router signals, if it can distinguish occluded from non-occluded signals.
From the perspective of accuracy, the calculating accuracy of fingerprinting will be
greatly affected by the density of the reference points. When the database granularity
achieves 5 feet, the corresponding average distance error could be 21.7 feet (6.62 meters)
(Prasithsangaree, Krishnamurthy, & Chrysanthis, 2002). In Wireless indoor tracking
system, a history-based tracking algorithm helps improving the accuracy to 3.89 meters
for quickly moving device (Zhou, 2006). When calculating position with trilateration
algorithm, distance conversion error becomes the largest error source, usually Kalman
Filter and Particle Filter are applied to trilateration algorithm to improve the accuracy,
which ranges from 2 to 6 meters depending on various kinds of systems.
Conclusions
Both fingerprinting and trilateration use estimated wireless signal strength to
determining the location. However, each determines position in different ways.
Fingerprinting requires a detailed signal strength database for each reference point that
can be compared with received signal strength in the field; the use of this method needs
to balance the accuracy and time-commitment for collecting data when creating signal
strength database. The trilateration technique is more flexible as the system calculates
device location in real-time and the system is more adaptable to environmental change
than fingerprinting. In real-world use, trilateration needs a correction factor to reduce
the effect of attenuation; fingerprinting, on the other hand, already considers
attenuation in the database creation process, which leads to a better accuracy in the
signal strength data for calculation.
References
Bahl, P., & Padmanabhan, V. (2000). RADAR: An in-building RF-based user location and
tracking system. Proceedings of the 19th Annual Joint Conference of the IEEE Computer
and Communications Societies. (pp.775-784).
Bell, S., Jung, W.R., & Krishnakumar, V. (2009). Proceedings of the 17th ACM SIGSPATIAL
International Conference on Advances in Geographic Information Systems. Seattle: ACM.
Li, B., Kam, J., Lui, J., & Dempster, A. (2007). Use of Directional Information in Wireless LAN
based indoor positioning. Proceedings of IGNSS(International Global Navigation
Satellite Systems Society) Symposium. Taipei:IEEE
Mok, E., & Retscher, G. (2007). Location determination using WiFi fingerprinting versus WiFi
trilateration. J. Locat. Based Serv., 1(2), 145-159. doi:
http://dx.doi.org/10.1080/17489720701781905
Prasithsangaree, P., Krishnamurthy, P., & Chrysanthis, P. (2002). On indoor position location
with wireless LANs. Proceedings of the 13th IEEE International Symposium on Personal,
Indoor and Mobile Radio Communications. Lisboa: Citeseer
Zandbergen, P. (2009). Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and
Cellular Positioning. Transactions in GIS, 13, 5-25.
Zhou, R. (2006). Wireless indoor tracking system (WITS). DoIT Conference on Software
Research. (pp.163-177).
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