Cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion  

作　　者：胡振涛 Hu Yumei Guo Zhen Wu Yewei 

机构地区：[1]Institute of Image Processing and Pattern Recognition,Henan University [2]College of Automation,Northwestern Polytechnical University

出　　处：《High Technology Letters》2016年第4期376-384,共9页高技术通讯（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.61300214);the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(No.13IRTSTHN021);the Post-doctoral Science Foundation of China(No.2014M551999); the Outstanding Young Cultivation Foundation of Henan University(No.0000A40366)

摘　　要：The GM-PHD framework as recursion realization of PHD filter is extensively applied to multitarget tracking system. A new idea of improving the estimation precision of time-varying multi-target in non-linear system is proposed due to the advantage of computation efficiency in this paper. First,a novel cubature Kalman probability hypothesis density filter is designed for single sensor measurement system under the Gaussian mixture framework. Second,the consistency fusion strategy for multi-sensor measurement is proposed through constructing consistency matrix. Furthermore,to take the advantage of consistency fusion strategy,fused measurement is introduced in the update step of cubature Kalman probability hypothesis density filter to replace the single-sensor measurement. Then a cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion is proposed. Capabilily of the proposed algorithm is illustrated through simulation scenario of multi-sensor multi-target tracking.The GM-PHD framework as recursion realization of PHD filter is extensively applied to multitarget tracking system. A new idea of improving the estimation precision of time-varying multi-target in non-linear system is proposed due to the advantage of computation efficiency in this paper. First,a novel cubature Kalman probability hypothesis density filter is designed for single sensor measurement system under the Gaussian mixture framework. Second,the consistency fusion strategy for multi-sensor measurement is proposed through constructing consistency matrix. Furthermore,to take the advantage of consistency fusion strategy,fused measurement is introduced in the update step of cubature Kalman probability hypothesis density filter to replace the single-sensor measurement. Then a cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion is proposed. Capabilily of the proposed algorithm is illustrated through simulation scenario of multi-sensor multi-target tracking.

关 键 词：multi-target tracking probability hypothesis density(PHD) cubature Kalman filter consistency fusion 

分 类 号：TN713[电子电信—电路与系统] TP212[自动化与计算机技术—检测技术与自动化装置]