Robust tracking algorithm for wireless sensor networks based on improved particle filter
Jie Wang, Qinghua Gao, Hongyu Wang, Hongyang Chen, Minglu Jin · Wireless Communications and Mobile Computing · 2010
Abstract Benefitting from its ability to estimate the target state's posterior probability density function (PDF) in complex nonlinear and non‐Gaussian circumstance, particle filter (PF) is widely used to solve the target tracking problem in wireless sensor networks. However, the traditional PF algorithm based on sequential importance sampling with re‐sampling will degenerate if the latest observation appear in the tail of the prior PDF or if the observation likelihood is too peaked in comparison with the prior. In this paper, we propose an improved particle filter which makes full use of the latest observation in constructing the proposal distribution. Thequality prediction functionis proposed to measure the quality of the particles, and only the high quality particles are selected and used to generate the coarse proposal distribution. Then, acentroid shift vectoris calculated based on the coarse proposal distribution, which leads the particles move towards the optimal proposal distribution. Simulation results demonstrate the robustness of the proposed algorithm under the challenging background conditions. Copyright © 2010 John Wiley & Sons, Ltd.