A Novel Approach for Tracking of a Mobile Node Based on Particle Filter and Trilateration
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:61541", author = "Muhammad Haroon Siddiqui and Muhammad Rehan Khalid", title = "A Novel Approach for Tracking of a Mobile Node Based on Particle Filter and Trilateration", abstract = "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", volume = "6", number = "4", pages = "444-6", }