Position and velocity tracking in mobile cellular networks using the particle filter

This paper presents a new method for tracking a mobile based on Aulin's wave scattering model This model takes into account non line of sight and multipath propagation environments, which are usually encountered in wireless fading channels. According to Aulin's model, the received instantaneous electric field at the base station is a nonlinear function of the mobile location and velocity. A method based on particle filtering (sequential Monte Carlo methods) that copes with nonlinearities in order to estimate the mobile location and velocity is proposed. In contrast to standard target tracking literature we do not rely on linearized motion models, measurement relations, and Gaussian assumptions. Numerical results are presented to evaluate the accuracy of the proposed method. They demonstrate significant accuracy improvement over known algorithms

[1]  N. Bergman,et al.  Auxiliary particle filters for tracking a maneuvering target , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

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

[3]  Joohwan Chun,et al.  Position tracking of mobiles in a cellular radio network using the constrained bootstrap filter , 2000, Proceedings of the IEEE 2000 National Aerospace and Electronics Conference. NAECON 2000. Engineering Tomorrow (Cat. No.00CH37093).

[4]  Thomas Kailath Lectures on linear least-squares estimation , 1976 .

[5]  Rudolf Mathar,et al.  Location tracking of mobiles in cellular radio networks , 1999 .

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

[7]  Fredrik Gustafsson,et al.  Range estimation using angle-only target tracking with particle filters , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[8]  Thomas B. Schön,et al.  Marginalized particle filters for mixed linear/nonlinear state-space models , 2005, IEEE Transactions on Signal Processing.

[9]  Fredrik Gustafsson,et al.  Particle filters for positioning, navigation, and tracking , 2002, IEEE Trans. Signal Process..

[10]  Theodore S. Rappaport,et al.  An overview of the challenges and progress in meeting the E-911 requirement for location service , 1998, IEEE Commun. Mag..

[11]  Rudolf Mathar,et al.  Estimating position and velocity of mobiles in a cellular radio network , 1997 .

[12]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[13]  T. Aulin A modified model for the fading signal at a mobile radio channel , 1979, IEEE Transactions on Vehicular Technology.

[14]  Charalambos D. Charalambous,et al.  An enhanced received signal level cellular location determination method via maximum likelihood and Kalman filtering , 2005, IEEE Wireless Communications and Networking Conference, 2005.