Constrained auxiliary particle filtering for bearings-only maneuvering target tracking  

作　　者：ZHANG Hongwei XIE Weixin 

机构地区：[1]Automatic Target Recognition Key Laboratory, Shenzhen University

出　　处：《Journal of Systems Engineering and Electronics》2019年第4期684-695,共12页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61773267);the Shenzhen Fundamental Research Project(JCYJ20170302145519524;20170818102503604)

摘　　要：To track the nonlinear,non-Gaussian bearings-only maneuvering target accurately online,the constrained auxiliary particle filtering(CAPF)algorithm is presented.To restrict the samples into the feasible area,the soft measurement constraints are implemented into the update routine via the1 regularization.Meanwhile,to enhance the sampling diversity and efficiency,the target kinetic features and the latest observations are involved into the evolution.To take advantage of the past and the current measurement information simultaneously,the sub-optimal importance distribution is constructed as a Gaussian mixture consisting of the original and modified priors with the fuzzy weighted factors.As a result,the corresponding weights are more evenly distributed,and the posterior distribution of interest is approximated well with a heavier tailor.Simulation results demonstrate the validity and superiority of the CAPF algorithm in terms of efficiency and robustness.To track the nonlinear, non-Gaussian bearings-only maneuvering target accurately online, the constrained auxiliary particle filtering(CAPF) algorithm is presented. To restrict the samples into the feasible area, the soft measurement constraints are implemented into the update routine via the 1 regularization.Meanwhile, to enhance the sampling diversity and efficiency, the target kinetic features and the latest observations are involved into the evolution. To take advantage of the past and the current measurement information simultaneously, the sub-optimal importance distribution is constructed as a Gaussian mixture consisting of the original and modified priors with the fuzzy weighted factors. As a result, the corresponding weights are more evenly distributed,and the posterior distribution of interest is approximated well with a heavier tailor. Simulation results demonstrate the validity and superiority of the CAPF algorithm in terms of efficiency and robustness.

关 键 词：BEARINGS-ONLY maneuvering target tracking SOFT measurement constraints CONSTRAINED AUXILIARY particle filtering(CAPF) 

分 类 号：O17[理学—数学]