自动化学报2016，Vol.42Issue(8)：1198-1206,9.DOI:10.16383/j.aas.2016.c150410
不确定系统改进的鲁棒协方差交叉融合稳态Kalman预报器
Modified Robust Covariance Intersection Fusion Steady-state Kalman Predictor for Uncertain Systems
摘要
Abstract
For linear discrete time multisensor systems with both stochastic parameter and noise variance uncertainties, the stochastic parametric uncertainty can be compensated by a fictitious noise, so the original system can be converted into the one with only uncertain noise variances. Based on the minimax robust estimation principle, the local robust steady-state Kalman predictors and the minimal upper bounds of their error variances are presented using the Lyapunov equation approach, and a modified robust covariance intersection (CI) fusion steady-state Kalman predictor and the minimal upper bound of its error variances are presented using the cross-covariances of the conservative local prediction errors. They overcome the disadvantages of the original CI fuser that the local estimates and their conservative error variances are assumed to be known, and the upper bound of fused estimation error variances has large conservativeness. The robustness of the robust local and fused predictors is proved, and it is proved that the robust accuracy of the modified CI fuser is higher than that of the original CI fuser and that of each local predictor. A simulation example shows the correctness and effectiveness of the proposed results.关键词
不确定系统/协方差交叉融合/极大极小鲁棒Kalman预报器/虚拟噪声/Lyapunov方程方法Key words
Uncertain system/covariance intersection (CI) fusion/minimax robust Kalman predictor/fictitious noise/Lyapunov equation approach引用本文复制引用
王雪梅,刘文强,邓自立..不确定系统改进的鲁棒协方差交叉融合稳态Kalman预报器[J].自动化学报,2016,42(8):1198-1206,9.基金项目
国家自然科学基金(60874063,60374026),黑龙江大学研究生创新科研项目(YJSCX2015-002HLJU)资助 （60874063,60374026）
