网络化不确定系统集中式融合鲁棒稳态估值器

对于一类在状态转移阵和系统观测阵中带相同的状态依赖乘性噪声、带噪声依赖乘性噪声、一步随机观测滞后、丢包和不确定噪声方差的多传感器网络化系统,文章研究其鲁棒集中式融合稳态滤波问题.应用增广方法将系统转换为带随机参数矩阵、相同过程和观测噪声的集中式融合系统.应用去随机化方法和虚拟噪声技术,系统进一步转化为仅带不确定噪声方差的集中式融合系统.根据极大极小鲁棒估计原理,本文提出了鲁棒集中式融合稳态Kalman估值器(预报器、滤波器和平滑器),证明了所提出的集中式融合估值器的鲁棒性,给出了鲁棒局部与集中式融合估值器之间的精度关系.本文提出了应用于多传感器多通道滑动平均(MA)信号估计的一个实例,给出了相应的鲁棒局部和集中式融合信号估值器.仿真实验验证了所提出方法的有效性和正确性.

Centralized fusion robust steady-state estimators for networked uncertain systems

The centralized fusion (CF) robust steady-state filtering problem is investigated for a class of multisensor networked systems with mixed uncertainties. The uncertainties include the same state-dependent multiplicative noises in state transition and system measurement matrices, noise-dependent multiplicative noises, one-step random delay, packet dropouts, and uncertain noise variances. By means of the augmentation approach, the system under study is converted into one with random parameter matrices and same process and measurement noises. Using the de-randomization approach and fictitious noise technique, the system is further converted into one with only uncertain noise variances. In the light of the minimax robust estimation principle, the robust CF steady-state Kalman estimators (predictor, filter, and smoother) are presented. The robustness of the proposed CF estimators is proved, the accuracy relations among the robust local and CF steady-state Kalman estimators are given. An example with application to multisensor multichannel moving average (MA) signal estimate is proposed, and the corresponding robust local and CF signal estimators are given. Simulation example verifies the effectiveness and correctness of the proposed method