自动化学报2007，Vol.33Issue(2)：156-163,8.
自校正对角阵加权信息融合Kalman预报器
Self-tuning Information Fusion Kalman Predictor Weighted by Diagonal Matrices and Its Convergence Analysis
摘要
Abstract
For the multisensor systems with unknown noise statistics, using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation models, and based on the solution of the matrix equations for correlation function, estimators of the noise variances are obtained, and under the linear minimum variance optimal information fusion criterion weighted by diagonal matrices, a self-tuning information fusion Kalman predictor is presented, which realizes the self-tuning decoupled fusion Kalman predictors for the state components. Based on the dynamic error system, a new convergence analysis method is presented for self-tuning fuser. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. It is strictly proved that if the parameter estimation of the MA innovation models is consistent, then the self-tuning fusion Kalman predictor will converge to the optimal fusion Kalman predictor in a realization, or with probability one, so that it has asymptotic optimality. It can reduce the computational burden, and is suitable for real time applications. A simulation example for a target tracking system shows its effectiveness.关键词
Multisensor information fusion/decoupled fusion/identification/self-tuning Kalman predictor/convergence analysisKey words
Multisensor information fusion/decoupled fusion/identification/self-tuning Kalman predictor/convergence analysis分类
信息技术与安全科学引用本文复制引用
邓自立,李春波..自校正对角阵加权信息融合Kalman预报器[J].自动化学报,2007,33(2):156-163,8.基金项目
Supported by National Natural Science Foundation of P.R. China (60374026) （60374026）
