Distributed Particle Filter for Target Tracking

Abstract

This paper investigates target tracking using a distributed particle filter over sensor networks. Gaussian mixture model is adopted to approximate the posterior distribution of weighted particles in this distributed particle filter. The parameters of Gaussian mixture model are exchanged between neighbor sensor nodes. Each node can obtain the Gaussian mixture model representing particle's posterior distribution through the parameter exchange. With the posterior distribution, the distributed particle filter can draw particles from it, predicted particles and observations, update particle weights, and re-sample particles based the predicted weights. The parameter exchange is key to implement the distributed operation. It is implemented by using an average consensus filter. Through this consensus filter, each sensor node can gradually diffuse its local statistics of weighted particles over the entire network and asymptotically obtain the estimated global statistics. The parameters of Gaussian mixture model can be calculated by using the estimated global statistics. Because the average consensus filter only requires that each sensor node communicate with its neighbors, the proposed distributed particle filter is scalable and robust. Simulations of tracking tasks in a sensor network with 100 sensor nodes are given.