Collaborative multimodel Rao-Blackwellised particle filter for target tracking in acoustic sensor networks

Abstract

An energy-aware, collaborative target tracking algorithm is proposed for ad-hoc wireless sensor networks. At every time step, current measurements from four sensors are chosen for target motion estimation and prediction. The algorithm is implemented distributively by passing sensing and computation operations from a subset of sensors to another. A robust multimodel Rao-Blackwellised particle filter algorithm is presented for tracking high maneuvering ground target in the sensor field. Not only is the proposed algorithm more computation efficient than generic particle filter for high dimension nonlinear and non-Gaussian estimation problems, but also it can tackle the target's maneuver perfectly by stratified particles sampling from a set of system models. In the simulation comparison, a high maneuvering target moves through an acoustic sensor network field. The target is tracked by both generic PF and the multimodel RBPF algorithms. The results show that our approach has great performance improvements, especially when the target is making maneuver.