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论文类型： 期刊论文

发表时间： 2012-07-01

发表刊物： WIRELESS COMMUNICATIONS & MOBILE COMPUTING

收录刊物： SCIE、EI、Scopus

卷号： 12

期号： 10

页面范围： 891-900

ISSN号： 1530-8669

关键字： wireless sensor networks; tracking; particle filter; proposal distribution

摘要： 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. The quality prediction function is 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, a centroid shift vector is 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 (C) 2010 John Wiley & Sons, Ltd.