Distributed particle filters for wireless sensor network target tracking

We propose two distributed particle filters to estimate and track the moving targets in a wireless sensor network. The observations by the sensors are divided into a set of disjoint uncorrelated cliques. The first distributed algorithm runs the local particle filters sequentially at each clique. The second distributed algorithm runs the local particle filters in parallel to obtain the local sufficient statistics, and then send these statistics to a centralized location through multi-hops to obtain the final estimates. The two distributed algorithms are both almost surely convergent. In addition, we proposed to use the local Gaussian mixture model (GMM) to approximate the posteriori distribution obtained from the local particle filter. By propagating the GMM parameters rather than belief, we achieve significant bandwidth and power consumption reduction. Very promising simulation results are reported as well.