Abstract—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. I. INTRODUCTION HE need for an efficient and accurate mobile station (MS) positioning system is growing day by day. This has been stressed by a recent federal order issued by the federal communications commission (FCC), which mandates all wireless service providers to provide public safety answering points with information to locate an emergency 911 caller with an accuracy of 100 meters for 67% of the cases [1]. It is also expected that the FCC will tighten its requirements in the near future [2]. Many other applications, such as vehicle fleet management, location sensitive billing, intelligent transport systems, fraud protection, and mobile yellow pages have driven the cellular industry to research new and promising technologies for MS positioning. The problem of estimating the location and velocity of a mobile subscriber by processing received information has been the subject of much research work over the last few years. The current literature and standards in estimating the location are based mostly on time signal information, such as Time Difference Of Arrival (TDOA), Enhanced Observed Time Differences (E-OTD), Observed Time Difference Of Arrival (OTDOA), Global Positioning System (GPS) etc., [3- 7]. However, not all of these methods meet the necessary needs imposed by specific services. In addition, most of them require new hardware since localization is not inherent in the current wireless systems, for instance, GPS demands a new receiver and TDOA, E-OTD, OTDOA require additional location measurement units in the network [8]. Adding extra hardware means extra cost for implementation, which can be reflected on both consumers and operators. Researchers have also suggested several mobile location methods based on signal power measurements such as in [9] and [10], where a certain minimization problem is solved numerically to get an initial estimate of the mobile position, and then a smoothing procedure such as linear regression [9], or the Kalman filter [10] are applied to obtain a more accurate estimate. In this paper, we propose a mobile tracking method using sequential Monte Carlo methods or particle filters [11], which employs the instantaneous electric field measurements based on the 3D multipath channel model of Aulin [12]. Aulin’s model accounts for multipath and non line of sight (NLOS) characteristics of the wireless channel as well as the dynamicity of the mobile user. The received instantaneous electric field in this model is a nonlinear function of the position and velocity of the mobile user. Nonlinear models in state equation and measurement relation and non-Gaussian noise assumption may lead to non-optimal solutions for linearized methods, such as, the extended Kalman filter (EKF). The particle filter approximates the optimal solution numerically based on the physical model, rather than applying an optimal filter to an approximate model such as in the EKF. It also provides general solutions to many problems where linearization and Gaussian approximations are intractable or yield low performance. The more nonlinear model or the more non-Gaussian noise, the more potential particle filters have, especially in applications where computational power is rather cheap and the sampling rate is moderate. In this paper, the particle filter algorithm is implemented for the classical Bayesian bootstrap method [13]. Aulin’s model postulates knowledge of the instantaneous received field at the mobile unit, which obtained through the circuitry of the mobile unit. The proposed algorithm takes into Position and Velocity Tracking in Mobile Cellular Networks Using the Particle Filter T Mohammed M. Olama ECE Department University of Tennessee 1508 Middle Dr. Knoxville, TN 37996, USA molama@utk.edu Chris S. Pendley ECE Department University of Tennessee 1508 Middle Dr. Knoxville, TN 37996, USA cpendle1@utk.edu Seddik M. Djouadi ECE Department University of Tennessee 1508 Middle Dr. Knoxville, TN 37996, USA djouadi@ece.utk.edu This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2006 proceedings. 1-4244-0270-0/06/$20.00 (c)2006 IEEE 165