Target Tracking by Using Particle Filter in Sensor Networks

This paper presents a distributed particle filter (DPF) over sensor networks. We propose two major steps to make a particle filter to work in a distributed way. The first step is the estimation of global mean and covariance of weighted particles by using an average consensus filter. Through this consensus filter, each sensor node can gradually diffuse its local mean and covariance of weighted particles over the entire network and asymptotically obtain the estimated global mean and covariance. The second step is the propagation of the estimated global mean and covariance through state transition distribution and likelihood distribution by using an unscented transformation (UT). Through this transformation, partial high order information of the estimated global mean and covariance can be incorporated into the estimates for non-linear models. Simulations of tracking tasks in a sensor network with 100 sensor nodes are given.