A RSS Based Indoor Tracking Algorithm via Particle Filter and Probability Distribution

Indoor positioning system that make use of received signal strength and existing wireless local area network infrastructure have recently been the focus for supporting location-based services. This paper presents a new indoor location tracking algorithm, that use:(1)signal strength probability distribution estimated by histogram method, addressing the noisy wireless channel, and (2)particle filter to deal with the nonlinear system model, which can approximate the optimal Bayesian estimate. Numerical simulation shows the new algorithm outperforms the tracking algorithm using Kalman filter in the former research.

[1]  Ismail Guvenc,et al.  Enhancements to RSS Based Indoor Tracking Systems Using Kalman Filters , 2003 .

[2]  Daniel P. Siewiorek,et al.  Determining User Location For Context Aware Computing Through the Use of a Wireless LAN Infrastructure , 2000 .

[3]  Prashant Krishnamurthy,et al.  Modeling of indoor positioning systems based on location fingerprinting , 2004, IEEE INFOCOM 2004.

[4]  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).

[5]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[6]  Prashant Krishnamurthy,et al.  Properties of indoor received signal strength for WLAN location fingerprinting , 2004, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004..

[7]  Moustafa Youssef,et al.  A Probabilistic Clustering-Based Indoor Location Determination System , 2002 .

[8]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[9]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[10]  Marko Helen,et al.  Using Calibration in RSSI- based Location Tracking System , 2005 .

[11]  Ted Kremenek,et al.  A Probabilistic Room Location Service for Wireless Networked Environments , 2001, UbiComp.