A New Distributed Particle Filtering for WSN Target Tracking

Distributed PF (DPF) was used due to the limitation of nodespsila computing capacity inferring to the target tracking in a wireless sensor network (WSN). In this paper, a novel filtering method - DPF* in WSN is proposed. Instead of transferring value and weight of particles, Gaussian mixture model (GMM) is used to approximate the posteriori distribution, and only GMM parameters need to be transferred which can reduce the bandwidth and power consumption. In order to use sampling information effectively, when target moving to the next cluster head region, the GMM parameters are transfer to the next cluster head, and combine with the new local GMM parameters to compose the new GMM parameters incrementally. The proposed DPF* is compared to some other DPF for WSN target tracking, and the experimental results show that not the precision is improved.