SCKF-STF-CN:a universal nonlinear filter for maneuver target tracking  

作　　者：Quan-bo GE Wen-bin LI Cheng-lin WEN 

机构地区：[1]Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China [2]State Key Lab of lndustrial Control Technology, Zhejiang University, Hangzhou 310027, China

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2011年第8期678-686,共9页浙江大学学报C辑（计算机与电子（英文版）

基　　金：supported by the National Natural Science Foundation of China (Nos.60934009,60804064,and 30800248);the China Post-doctoral Science Foundation (No.20100471727);the Science and Technology Department of Zhejiang Province,China (No.2009C34016)

摘　　要：Square-root cubature Kalman filter (SCKF) is more effective for nonlinear state estimation than an unscented Kalman filter.In this paper,we study the design of nonlinear filters based on SCKF for the system with one step noise correlation and abrupt state change.First,we give the SCKF that deals with the one step correlation between process and measurement noises,SCKF-CN in short.Second,we introduce the idea of a strong tracking filter to construct the adaptive square-root factor of the prediction error covariance with a fading factor,which makes SCKF-CN obtain outstanding tracking performance to the system with target maneuver or abrupt state change.Accordingly,the tracking performance of SCKF is greatly improved.A universal nonlinear estimator is proposed,which can not only deal with the conventional nonlinear filter problem with high dimensionality and correlated noises,but also achieve an excellent strong tracking performance towards the abrupt change of target state.Three simulation examples with a bearings-only tracking system are illustrated to verify the efficiency of the proposed algorithms.Square-root cubature Kalman filter （SCKF） is more effective for nonlinear state estimation than an unscented Kalman filter. In this paper, we study the design of nonlinear filters based on SCKF for the system with one step noise correlation and abrupt state change. First, we give the SCKF that deals with the one step correlation between process and measurement noises, SCKF-CN in short. Second, we introduce the idea of a strong tracking filter to construct the adaptive square-root factor of the prediction error eovariance with a fading factor, which makes SCKF-CN obtain outstanding tracking performance to the system with target maneuver or abrupt state change. Accordingly, the tracking performance of SCKF is greatly improved. A universal nonlinear estimator is proposed, which can not only deal with the conventional nonlinear filter problem with high dimensionality and correlated noises, but also achieve an excellent strong tracking performance towards the abrupt change of target state. Three simulation examples with a bearings-only tracking system are illustrated to verify the efficiency of the proposed algorithms.

关 键 词：Nonlinear system Maneuver target tracking Correlated noises Square-root cubature Kalman filter (SCKF) Strong tracking filtering (STF) 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]