Energy efficient collaborative target tracking by Gaussian Rao-Blackwellised particle filter in wireless sensor networks

Abstract

Target tracking is one of the main applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of sensor nodes. A new energy efficient collaborative target tracking algorithm via particle filtering (PF) is presented. Assuming the network infrastructure is cluster-based, collaborative scheme is implemented through passing sensing and computation operations from one active cluster to another and an event driven cluster reforming approach is also proposed for evening energy consumption distribution. At each time step, measurements from three sensors are chosen at the current active cluster head to estimate and predict the target motion and the results are propagated among cluster heads to the sink. In order to save the communication and computation resource, we present a new particle filter algorithm called Gaussian Rao-Blackwellised Particle Filter (GRBPF), which approximate the posterior distributions by Gaussians and only the mean and covariance of the Gaussians need to be communicated among cluster heads when target enter another cluster. The GRBPF algorithm is also more computation efficient than generic PF by dropping the resampling step. In the simulation comparison, a target moves through the sensor network field and is tracked by both generic PF and the GRBPF algorithm using our proposed collaborative scheme. The results show that the latter works very well for target tracking in wireless sensor networks and the total communication burden is substantially reduced, so as to prolong the lifetime of wireless sensor networks.