Decoupled Wiener state fuser for descriptor systems

作     者：Chenjian RAN Zili DENG 

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

出 版 物：《控制理论与应用（英文版）》 (JOURNAL OF CONTROL THEORY AND APPLICATIONS)

年 卷 期：2008年第6卷第4期

页      面：365-371页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：the National Natural Science Foundation of China (No.60874063) the Innonvation Scientific Research Fundation for Graduate Students of Heilongjiang Province (No.YJSCX2008-018HLJ) 

主　　题：Multisensor information fusion Weighted fusion Decoupled fusion Descriptor system Wiener statefuser White noise estimator ARMA innovation model Modern time series analysis method 

摘      要：By the modem time series analysis method, based on the autoregressive moving average （ARMA） innovation models and white noise estimation theory, using the optimal fusion rule weighted by diagonal matrices, a distributed descriptor Wiener state fuser is presented by weighting the local Wiener state estimators for the linear discrete stochastic descriptor systems with multisensor. It realizes a decoupled fusion estimation for state components. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors are presented based on cross-covariances among the local innovation processes, input white noise, and measurement white noises. It can handle the fused filtering, smoothing, and prediction problems in a unified framework. Its accuracy is higher than that of each local estimator. A Monte Carlo simulation example shows its effectiveness and correctness.