Improved fingerprint algorithm for WLAN-based vehicle positioning

Reliable and accurate vehicle position information is important for autonomous systems. The increased popularity of wireless networks has enabled the development of positioning techniques that rely on WLAN signal strength. Fingerprint architecture is one of the most viable solutions for Received Signal Strength (RSS)-based positioning. The most challenging aspect of the fingerprint based method is to formulate a distance calculation that can measure similarity between the observed RSS and the known RSS fingerprints. In this paper we proposed an improved fingerprint-based algorithm that incorporates the spatial diversity and the road constraints for vehicle positioning. We present how to apply the properties of the spatial diversity and the road constraints to enhance the robustness and the accuracy of the fingerprint algorithm. To further improve the accuracy of the fingerprint algorithm, a Dead Reckoning (DR) system, which has been widely used for vehicle navigation, has been integrated with the WLAN-based positioning system. We have conducted extensive field tests and simulations for the proposed positioning algorithm, and key outcomes are given out. It has been demonstrated by the results that our algorithm could significantly improve the performance of WLAN-based vehicle positioning system.

[1]  François Marx,et al.  Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning , 2006, EURASIP J. Adv. Signal Process..

[2]  Shih-Hau Fang,et al.  A Novel Algorithm for Multipath Fingerprinting in Indoor WLAN Environments , 2008, IEEE Transactions on Wireless Communications.

[3]  Wu Chen,et al.  Positioning and tracking construction vehicles in highly dense urban areas and building construction sites , 2007 .

[4]  Sergios Theodoridis,et al.  A Novel Efficient Cluster-Based MLSE Equalizer for Satellite Communication Channels with-QAM Signaling , 2006, EURASIP J. Adv. Signal Process..

[5]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[6]  Yu-Chee Tseng,et al.  A Scrambling Method for Fingerprint Positioning Based on Temporal Diversity and Spatial Dependency , 2008, IEEE Transactions on Knowledge and Data Engineering.

[7]  Salvatore Cavalieri.  WLAN-based Outdoor Localisation Using Pattern Matching Algorithm , 2007, Int. J. Wirel. Inf. Networks.

[8]  Bill N. Schilit,et al.  Place Lab: Device Positioning Using Radio Beacons in the Wild , 2005, Pervasive.

[9]  渡辺 亮平,et al.  Sequential Monte Carlo , 2005, Nonlinear Time Series Analysis.

[10]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .