Modified joint probabilistic data association with classification-aided for multitarget tracking

作     者：Ba Hongxin Cao Lei He Xinyi Cheng Qun 

作者机构：Air Force Command Coll. Beijing 100097 P. R. China Inst. of Command Automation PLA Univ. of Science & Technology Nanjing 210007 P. R. China Inst. of Combat Systems Naval Academy of Armament Beijing 100073 P. R. China Scientific Research Dept. PLA Univ. of Science & Technology Nanjing 210007 P. R. China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2008年第19卷第3期

页      面：434-439页

核心收录：

学科分类：11[军事学] 0810[工学-信息与通信工程] 1105[军事学-军队指挥学] 08[工学] 081002[工学-信号与信息处理] 110503[军事学-军事通信学] 

基　　金：Defense Advanced Research Project "the Techniques of Information Integrated Processing and Fusion" in the Eleventh Five-Year Plan (513060302) 

主　　题：multi-target tracking data association joint probabilistic data association classification information,track coalescence maneuvering target. 

摘      要：Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.