Distributive Target Tracking in Wireless Sensor Networks under Measurement Origin Uncertainty

Abstract

This paper addresses the problem of tracking a single target under measurement uncertainty due to clutters and missed detections in wireless sensor networks. By adopting the particles' representation of the probability density function of target state, this paper develops a particle filter (PF) and probabilistic data association filter (PDAF) hybrid tracking algorithm, name as PF-PDAF. PF-PDAF extends the well-known PDAF to the general nonlinear system. Based on the hierarchical sensor network architecture, the distributive PF- PDAF is also implemented. Moreover, the posterior Cramer-Rao lower bound (PCRLB) is computed to provide a theoretical bound on the tracking performance of the developed algorithms. Simulation results are provided.