Abstract
The location of people, mobile terminals and equipments is highly desirable for operational enhancements and safety reasons 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 RSS, AOA and 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 station location using narrowband channel measurement results applied to an artificial neural network (ANN). The proposed system learns off-line the location ‘signatures’ from the extracted location-dependent features of the measured data for LOS and NLOS situations. It then matches on-line the observation received from a mobile station 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 45% of untrained data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Nerguizian, C., Despins, C., Affès, S.: A Framework for Indoor Geolocation using an Intelligent System. In: 3rd IEEE Workshop on WLANs, Boston, USA (September 2001), http://www.wlan01.wpi.edu/proceedings/wlan44d.pdf
Bahl, P., Padmanabhan, V.N.: RADAR: An In-Building RF-based User Location and Tracking System. In: Proceedings of IEEE INFOCOM 2000, Tel Aviv, Israel (March 2000)
Microsoft Corporation, http://www.microsoft.com
Ekahau Inc., http://www.ekahau.com
Wireless Internet and Location Management Architecture, http://www.wilmaproject.org
Battiti, R., Villani, A., Nhat, T.L.: Neural Network Models for Intelligent Network: Deriving the Location from Signal Patterns. In: Autonomous Intelligent Networks and Systems, UCLA, Los Angeles, USA (May 2002)
US Wireless Corporation, http://www.uswcorp.com
VTT Technical Research Centre, http://www.vtt.fi
Laitinen, H., Nordström, T., Lähteenmäki, J.: Location of GSM Terminals using a Database of Signal Strength Measurements. In: URSI XXV National Convention on Radio Science, Helsinki, Finland (September 2000)
Ahonen, S., Lähteenmäki, J., Laitinen, H., Horsmanheimo, S.: Usage of Mobile Location Techniques for UMTS Network Planning in Urban Environment. In: IST Mobile and Wireless Telecommunications Summit 2002, Thessaloniki, Greece (June 2002)
Nypan, T., Gade, K., Maseng, T.: Location using Estimated Impulse Responses in a Mobile Communication System. In: 4th Nordic Signal Processing Symposium (NORSIG 2001), Trondheim, Norway (October 2001)
Wax, M., Hilsenrath, O.: Signature Matching for Location Determination in Wireless Communication Sustems. U.S. Patent 6,112,095
Djadel, M., Despins, C., Affès, S.: Narrowband Propagation Characteristics at 2.45 and 18 GHz in Underground Mining Environments. In: Proceedings IEEE GLOBECOM 2002, Taipai, Taïwan (November 2002)
Haykin, S.: Neural Network, a Comprehensive Foundation. Macmillan, Basingstoke (1994)
Demuth, H., Beale, M.: Neural Network Toolbox for use with Matlab (User’s Guide). The MathWorks Inc. (1998)
Caffery Jr., J.J., Stüber, G.L.: Overview of Radiolocation in CDMA Cellular Systems. IEEE Communications Magazine (April 1998)
Nerguizian, C., Despins, C., Affès, S.: Geolocation in Mines with an Impulse Response Fingerprinting Technique and Neural Networks. submitted to the IEEE Transactions on Wireless Communications (December 2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nerguizian, C., Despins, C., Affès, S. (2004). Indoor Geolocation with Received Signal Strength Fingerprinting Technique and Neural Networks. In: de Souza, J.N., Dini, P., Lorenz, P. (eds) Telecommunications and Networking - ICT 2004. ICT 2004. Lecture Notes in Computer Science, vol 3124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27824-5_114
Download citation
DOI: https://doi.org/10.1007/978-3-540-27824-5_114
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22571-3
Online ISBN: 978-3-540-27824-5
eBook Packages: Springer Book Archive