MULTITARGET STATE AND TRACK ESTIMATION FOR THE PROBABILITY HYPOTHESES DENSITY FILTER  

作　　者：Liu Weifeng Han Chongzhao Lian Feng Xu Xiaobin Wen Chenglin 

机构地区：[1]School of Electronic Information Engineering, Xi＇an Jiaotong University, Xi＇an 710049, China [2]Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China

出　　处：《Journal of Electronics(China)》2009年第1期2-12,共11页电子科学学刊（英文版）

基　　金：Supported by the National Key Fundamental Research & Development Program of China (2007CB11006);the Zhejiang Natural Science Foundation (R106745, Y1080422)

摘　　要：The particle Probability Hypotheses Density (particle-PHD) filter is a tractable approach for Random Finite Set (RFS) Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existing approaches combine the data association step to solve this problem. This paper proposes an algorithm which does not need the association step. Our basic ideal is based on the clustering algorithm of Finite Mixture Models (FMM). The intensity distribution is first derived by the particle-PHD filter, and then the clustering algorithm is applied to estimate the multitarget states and tracks jointly. The clustering process includes two steps: the prediction and update. The key to the proposed algorithm is to use the prediction as the initial points and the convergent points as the es- timates. Besides, Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC) ap- proaches are used for the FMM parameter estimation.The particle Probability Hypotheses Density （particle-PHD） filter is a tractable approach for Random Finite Set （RFS） Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existing approaches combine the data association step to solve this problem. This paper proposes an algorithm which does not need the association step. Our basic ideal is based on the clustering algorithm of Finite Mixture Models （FMM）. The intensity distribution is first derived by the particle-PHD filter, and then the clustering algorithm is applied to estimate the multitarget states and tracks jointly. The clustering process includes two steps： the prediction and update. The key to the proposed algorithm is to use the prediction as the initial points and the convergent points as the estimates. Besides, Expectation-Maximization （EM） and Markov Chain Monte Carlo （MCMC） approaches are used for the FMM parameter estimation.

关 键 词：Probability Hypotheses Density （PHD） Particle-PHD filter State and track estimation Finite mixture models 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]