Bandwidth-Efficient Target Tracking In Distributed Sensor Networks Using Particle Filters

This paper considers the problem tracking a moving target in a multisensor environment using distributed particle filters (DPFs). Particle filters have a great potential for solving highly nonlinear and non-Gaussian estimation problems, in which the traditional Kalman filter (KF) and extended Kalman filter (EKF) generally fail. How ever, in a sensor network, the implementation of distributed particle filters requires huge communications between local sensor nodes and the fusion center. To make the DPF approach feasible for real time processing and to reduce communication requirements, we approximate a posteriori distribution obtained from the local particle filters by a Gaussian mixture model (GMM). We propose a modified EM algorithm to estimate the parameters of GMMs obtained locally. These parameters are transmitted to the fusion center where the best linear unbiased estimator (BLUE) is used for fusion. Simulation results are presented to illustrate the performance of the proposed algorithm