A distributed particle filter for acustic target tracking in wireless sensor networks

Abstract

One of the applications of wireless sensor networks is target tracking. There are several methods to target tracking and among these methods, particle filter has high capability in solving nonlinear/non-Gaussian systems. Particle filter is one of the methods for Bayesian recursive estimation for position estimation in wireless sensor networks. Clustered management of dense networks is a famous known method and has many benefits in comparison with other management schemes of sensor nodes. In this paper we designed a new distributed version of particle filtering which is more compatible with dynamic clustering of nodes. Our method decrease the processing overhead of sensor nodes and output results are more accurate than current distributed versions of particle filtering in target tracking applications.