SELF-TUNING MEASUREMENT FUSION KALMAN FILTER WITH CORRELATED MEASUREMENT NOISES

作     者：Gao Yuan Ran Chenjian Deng Zili 

作者机构：Department of Automation Heilongjiang University Harbin 150080 China 

出 版 物：《Journal of Electronics(China)》 (电子科学学刊（英文版）)

年 卷 期：2009年第26卷第5期

页      面：614-622页

学科分类：02[经济学] 07[理学] 08[工学] 070103[理学-概率论与数理统计] 071102[理学-系统分析与集成] 0711[理学-系统科学] 0202[经济学-应用经济学] 020208[经济学-统计学] 0714[理学-统计学（可授理学、经济学学位）] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0701[理学-数学] 081103[工学-系统工程] 

基　　金：Supported by the National Natural Science Foundation of China (No.60874063) Science and Technology Research Foundation of Heilongjiang Education Department (No.11521214) Open Fund of Key Laboratory of Electronics Engineering, College of Heilongjiang Province (Heilongjiang University) 

主　　题：Correlation function method Multisensor measurement fusion Self-tuning Kalman filter Convergence in a realization 

摘      要：For the multisensor system with correlated measurement noises and unknown noise statistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser.