NDLI: Mobile robot geolocation with received signal strength (RSS) fingerprinting technique and neural networks

Content Provider	IEEE Xplore Digital Library
Author	Nerguizian, C. Belkhous, S. Azzouz, A. Nerguizian, V. Saad, M.
Copyright Year	2004
Description	Author affiliation: Ecole Polytech., Montreal, Que., Canada (Nerguizian, C.; Belkhous, S.; Azzouz, A.)
Abstract	The location of a mobile robot is highly desirable for operational enhancements in indoor environments. In an in-building environment, the multipath caused by reflection and diffraction, and the obstruction and/or the blockage of the shortest path between transmitter and receiver are the main sources of range measurement errors. Due to the harsh indoor environment, unreliable measurements of location metrics such as received signal strength (RSS), angle of arrival (AOA) and time or time difference of arrival TOA/TDOA result in the deterioration of the positioning performance. Hence, alternatives to the traditional parametric geolocation techniques have to be considered. In this paper, we present a method for mobile robot location using WLAN's received power (RSS) data applied to an artificial neural network (ANN). The proposed system learns off-line the location RSS 'signatures' for line of sight (LOS) and non-line of sight (NLOS) situations. It then matches on-line the observation received from a mobile robot against the learned set of 'signatures' to accurately locate its position. The location precision of the proposed system, applied in an in-building environment, has been found to be 0.5 meter for 90% of trained data and about 5 meters for 58% of untrained data.
Starting Page	1183
Ending Page	1185
File Size	581319
Page Count	3
File Format	PDF
ISBN	0780386620
DOI	10.1109/ICIT.2004.1490728
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-08
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mobile robots Fingerprint recognition Neural networks Indoor environments Artificial neural networks Reflection Diffraction Transmitters RAKE receivers Measurement errors
Content Type	Text
Resource Type	Article

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