带丢失观测和不确定噪声方差系统改进的鲁棒协方差交叉融合稳态Kalman滤波器

对带丢失观测和不确定噪声方差的线性定常多传感器系统,引入虚拟噪声将原系统转化为仅带不确定噪声方差的系统.根据极大极小鲁棒估值原理,用Lyapunov方程方法提出局部鲁棒稳态Kalman滤波器及其实际方差最小上界,并利用保守的局部滤波误差互协方差,提出一种改进的鲁棒协方差交叉(covariance intersection,CI)融合稳态Kalman滤波器及其实际方差最小上界.证明了所提出的鲁棒局部和融合滤波器的鲁棒性,并证明了改进的CI融合器鲁棒精度高于原始CI融合鲁棒精度,且高于每个局部滤波器的鲁棒精度.一个仿真例子验证所提出结果的正确性和有效性.

Modified robust covariance intersection fusion steady-state Kalman filter for systems with missing measurements and uncertain noise variances

For the linear time-invariant multisensor system with missing measurements and uncertain noise variances, by introducing the fictitious noises, the original system can be converted into one with only uncertain noise variances. According to the minimax robust estimation principle, using the Lyapunov equation approach, the local robust steady-state Kalman filters and the minimal upper bounds of their actual variances are presented, and a modified robust covariance intersection (CI) fusion steady-state Kalman filter and the minimal upper bound of its actual variances are presented using the conservative cross-covariances of the local filtering errors. The robustness of the robust local and fused filters 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 higher than that of each local filter. A simulation example verifies correctness and effectiveness of the proposed results.