This paper studies the estimation problem for multisensor networked systems with mixed uncertainties, which include colored noises, same multiplicative noises in system parameter matrices, uncertain noise variances, as well as the one-step random delay (OSRD) and packet dropouts (PDs). This study utilizes the centralized fusion (CF) algorithm to combing all information received by each sensor, which improve the accuracy of the estimation. By using the augmentation method, de-randomization method and fictitious noise techniques, the original uncertain system is transformed into an augment model with only uncertain noise variances. Then, for all uncertainties within the allowable range, the robust CF steady-state Kalman estimators (predictor, filter, and smoother) are presented based on the worst-case CF system, in light of the minimax robust estimation principle. To demonstrate the robustness of the proposed CF estimators, the non-negative definite matrix decomposition method and Lyapunov equation approach are employed. It is proved that the robust accuracy of CF estimator is higher than that of each local estimator. Finally, the simulation example applied to the uninterruptible power system (UPS) with colored noises and multiple uncertainties illustrates the effectiveness of the proposed CF robust estimation algorithm.

本文研究了具有混合不确定性的多传感器网络系统的估计问题，包括有色噪声、系统参数矩阵中的相同乘性噪声、不确定的噪声方差，以及单步随机延迟（OSRD）和丢包（PDs）。本研究利用集中融合（CF）算法对每个传感器接收到的所有信息进行组合，从而提高了估计的准确性。通过使用增强方法、去随机化方法和虚拟噪声技术，将原来的不确定系统转化为只有不确定噪声方差的增强模型。然后，对于允许范围内的所有不确定性，基于最坏情况 CF 系统，提出了稳健的 CF 稳态卡尔曼估计器（预测器、滤波器和平滑器），根据极小极大鲁棒估计原理。为了证明所提出的 CF 估计器的稳健性，采用了非负定矩阵分解方法和 Lyapunov 方程方法。证明了CF估计器的鲁棒精度高于每个局部估计器。最后，应用在有色噪声和多不确定性的不间断电源系统（UPS）的仿真例子说明了所提出的CF鲁棒估计算法的有效性。