Single-step-lag OOSM algorithm based on unscented transformation  

作　　者：CHEN JinGuang LI Jie GAO XinBo 

机构地区：[1]School of Electronic Engineering, Xidian University, Xi 'an 710071, China [2]School of Computer Science, Xi'an Polytechnic University, Xi'an 710048, China

出　　处：《Science China(Information Sciences)》2011年第3期664-673,共10页中国科学（信息科学）（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant Nos. 60832005, 60702061,60771068);the Ph.D. Programs Foundation of Ministry of Education of China (Grant No. 20090203110002);the Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2009JM8004)

摘　　要：Aiming at the out-of-sequence measurement （OOSM） problem, the update equations of the nonlinear single-step-lag OOSM are derived based on the existing methods. By introducing the unscented transformation （UT）, the covariance between state vector and corresponding measurement vector are computed such that the single-step-lag OOSM can be effectively solved under the nonlinear Gaussian system with nonlinear measurement equation and linear dynamic equation. Furthermore, a single-step-lag OOSM fusion algorithm based on UT is presented to confront the problem of the single-step-lag OOSM in multi-sensor system. The proposed algorithm has some advantages over the EKF A1 based on the extended Kalman filter frame and the optimal method without lags. For example, it can be used when the Jacobian matrix or the Hessian matrix of nonlinear measurement equation is nonexistent; its filtering performance is better; and its complexity has the same order of magnitude as that of the EKF A1 algorithm.Aiming at the out-of-sequence measurement （OOSM） problem, the update equations of the nonlinear single-step-lag OOSM are derived based on the existing methods. By introducing the unscented transformation （UT）, the covariance between state vector and corresponding measurement vector are computed such that the single-step-lag OOSM can be effectively solved under the nonlinear Gaussian system with nonlinear measurement equation and linear dynamic equation. Furthermore, a single-step-lag OOSM fusion algorithm based on UT is presented to confront the problem of the single-step-lag OOSM in multi-sensor system. The proposed algorithm has some advantages over the EKF A1 based on the extended Kalman filter frame and the optimal method without lags. For example, it can be used when the Jacobian matrix or the Hessian matrix of nonlinear measurement equation is nonexistent; its filtering performance is better; and its complexity has the same order of magnitude as that of the EKF A1 algorithm.

关 键 词：state estimation nonlinear filtering out-of^sequence measurements unscented transformation data fusion 

分 类 号：TP274.2[自动化与计算机技术—检测技术与自动化装置] U455.48[自动化与计算机技术—控制科学与工程]