Maneuvering Target Tracking in Dense Clutter Based on Particle Filtering

查看全文 作　　者：YANG [1]Xiaojun;XING [2]Keyi;FENG [1]Xingle

机构地区：[1]School of Information Engineering, Chang'an University, Xi'an 710064, China;[2]The National Key Laboratory for Manufacturing System Engineering, Xi 'an Jiaotong University, Xi'an 710049 China

出　　处：《Chinese Journal of Aeronautics》索引2011年第24卷第2期,共10页

基　　金：National Natural Science Foundation of China (60975028);National High-tech Research and Development Program (2009AA112203);Fundamental Research Funds for the Central Universities (CHD2009JC037);Natural Science Basic Research Plan in Shaanxi Province (2006F12) 摘　　要：An improved particle filtering(IPF) is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode prior probabilities and measure-ment-origin uncertainty.Within the framework of a hybrid state estimation,each particle samples a discrete mode from its poste-rior distribution and the continuous state variables are approximated by a multivariate Gaussian mixture that is updated by an unscented Kalman filtering(UKF).The uncertainty of measurement origin is solved by Monte Carlo probabilistic data associa-tion method where the distribution of interest is approximated by particle filtering and UKF.Correct data association and precise behavior mode detection are successfully achieved by the proposed method in the environment with heavy clutter and very low mode prior probability.The performance of the proposed filter is examined and compared by Monte Carlo simulation over typical target scenario for various clutter densities.The simulation results show the effectiveness of the proposed filter. 关键词：机动目标跟踪粒子滤波 UNSCENTED卡尔曼滤波杂波密集蒙特卡罗模拟先验概率测量不确定度

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Maneuvering Target Tracking in Dense Clutter Based on Particle Filtering

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