Hidden Markov Model Based Tracking of a Proxy RP in Wi-Fi Localization

In indoor localization of a mobile device (MD) in WLAN, a proxy reference point (RP) plays the role of representing the dynamically changing channel parameters between an access point (AP) and a MD. We present a hidden Markov model (HMM) based method of tracking the proxy RP for a MD. First, we examined the performance of finding the proxy RP for a stationary MD as we vary the number of APs, the deployment style of APs and the noise strength in RSS. Then, for the tracking of proxy RP for a moving MD, we developed a HMM-based algorithm, which provides the relation between the RSS vector and its proxy RP in a probabilistic manner. Both in computer simulation and in real measurements, HMM-based tracking proved to have much better performance than point-by-point decision by Euclidean distance between RSS vectors.

[1]  Henry Tirri,et al.  Emerging Location Aware Broadband Wireless Adhoc Networks , 2004 .

[2]  Ann-Chen Chang,et al.  Covariance Shaping Least-Squares Location Estimation Using TOA Measurements , 2007, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[3]  Hing Cheung So,et al.  Constrained Location Algorithm Using TDOA Measurements , 2003, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[4]  Henry Tirri,et al.  Probabilistic Methods for Location Estimation in Wireless Networks , 2005 .

[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]  Michael Wallbaum,et al.  Markov Localization of Wireless Local Area Network Clients , 2004, WONS.

[7]  S. Seidel,et al.  914 MHz path loss prediction models for indoor wireless communications in multifloored buildings , 1992 .

[8]  Rong Peng,et al.  Angle of Arrival Localization for Wireless Sensor Networks , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.

[9]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[10]  Thorsten Vaupel,et al.  A Hidden Markov Model for pedestrian navigation , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.