Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32451
A Novel Approach for Tracking of a Mobile Node Based on Particle Filter and Trilateration

Authors: Muhammad Haroon Siddiqui, Muhammad Rehan Khalid


This paper evaluates the performance of a novel algorithm for tracking of a mobile node, interms of execution time and root mean square error (RMSE). Particle Filter algorithm is used to track the mobile node, however a new technique in particle filter algorithm is also proposed to reduce the execution time. The stationary points were calculated through trilateration and finally by averaging the number of points collected for a specific time, whereas tracking is done through trilateration as well as particle filter algorithm. Wi-Fi signal is used to get initial guess of the position of mobile node in x-y coordinates system. Commercially available software “Wireless Mon" was used to read the WiFi signal strength from the WiFi card. Visual Cµ version 6 was used to interact with this software to read only the required data from the log-file generated by “Wireless Mon" software. Results are evaluated through mathematical modeling and MATLAB simulation.

Keywords: Particle Filter, Tracking, Wireless Local Area Network, WiFi, Trilateration

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1931


[1] A.Chakraborty, "A distributed architecture for mobile, locationdependent applications," M.S. thesis, Massachusetts Institute of Technology, May 2000.
[2] T.Cutler, "Wireless ethernet and how to use it," in The Online Industrial Ethernet Book, Issue 5. 1999.
[3] A.Neskovic, N.Neskovic, and G.Paunovic, "Modern approaches in modeling of mobile radio systems propagation environment," IEEE Communications Surveys, 2000.
[4] H.Hashemi, "The indoor radio propagation channel," in Proceedings of the IEEE, July 1993, vol. 81, pp. 943-968
[5] D.Fox, W.Burgard, and S.Thrun, "Markov localization for mobile robots in dynamic environments," Journal of Artificial Intelligence Research, vol. 11, pp. 391-427, 1999.
[6] M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Trans. Signal Process., vol. 50, no. 2, pp. 174-188, Feb. 2002.
[7] P. M. Djuric, M. Vemula, and M. F. Bugallo, "Target tracking by particle filtering in binary sensor networks," IEEE Trans. Signal Process., vol. 56, no. 6, pp. 2229-2238, Jun. 2006.
[8] D.Fox, S.Thrun, F.Dellaert, andW.Burgard, "Particle filters for mobile robot localization," in Sequential Monte Carlo Methods in Practice. Springer-Verlag, New York, 2001.
[9] J.J.Leonard and Durrant-Whyte, "Mobile robot localization by tracking geometric beacons," IEEE Transactions on Robotics and Automation, vol. 2, pp. 1080-1087, 1991.
[10] A.M.Ladd, K.E.Bekris, G.Marceau, A.Rudys, D.S.Wallach, and L.E.Kavraki, "Robotics-based location sensing for wireless ethernet," in Eighth ACM International Conference of Mobile Computing and Networking (MOBICOM 2002), Atlanta,GA, September 2002.
[11] P.Bahl and V.N.Padmanabhan, "Radar: An in-building rfbased user location and tracking system," in Proceedings of IEEE Infocom 2000, Tel- viv,Israel, March 2000, vol. 2, pp. 775-784.
[12] T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, and J. Sievanan. A probabilistic approach to wlan user location estimation. International Journal of Wireless Information Networks, 9(3), July 2002.
[13] Ioannis M. Rekleitis, "A Particle Filter Tutorial for Mobile Robot Localization".Technical Report TR-CIM-04-02,Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada, 2004.
[14] A. Goldsmith, "Wireless Communications", Cambridge University Press, 2005.