In this article, the robust distributed fusion Kalman filtering problems are addressed for the networked mixed uncertain multisensor systems with random one‐step measurement delays, multiplicative noises, and uncertain noise variances. A new augmented state approach with fictitious measurement noises modeled by the first‐order moving average models is presented, by which the original system is transformed into a standard uncertain system only with uncertain‐variance fictitious white noises. Based on the minimax robust estimation principle and Kalman filtering theory, a universal integrated covariance intersection (ICI) fusion approach is presented in the sense that first of all the robust local estimators and their conservative error variances and crosscovariances are presented, and then integrating the local estimation information yields ICI fusers. An extended Lyapunov equation approach with two kinds of Lyapunov equations is presented in order to prove the robustness and to compute fictitious noise statistics. Applying these approaches, the minimax robust local, ICI, and fast ICI fused Kalman estimators (predictor, filter, and smoother) are presented, such that for all admissible uncertainties, their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. Their robustness, accuracy relations, and convergence are also proved. The proposed ICI fusers improve the robust accuracies and overcome the drawbacks of the original covariance intersection fusers, such that the robust local estimators and their conservative variances are assumed to be known, and their conservative crosscovariances are ignored. Two simulation examples applied to the offshore platform system verify their correctness, effectiveness, and applicability.

中文翻译：

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适用于具有随机测量延迟，乘法噪声和不确定噪声方差的网络系统的鲁棒集成协方差交点融合卡尔曼估计器

本文针对具有随机单步测量延迟，乘法噪声和不确定噪声方差的网络混合不确定多传感器系统，解决了鲁棒的分布式融合卡尔曼滤波问题。提出了一种由一阶移动平均模型模拟的带有虚拟测量噪声的增强状态方法，该方法将原始系统转换为仅具有不确定方差虚拟白噪声的标准不确定系统。基于最小极大鲁棒估计原理和卡尔曼滤波理论，提出了一种通用的集成协方差交点（ICI）融合方法，即首先给出了鲁棒的局部估计器及其保守的误差方差和互协方差，然后整合本地估计信息即可得出ICI融合器。为了证明其鲁棒性并计算虚拟噪声统计量，提出了一种扩展的李雅普诺夫方程方法，该方法具有两种李雅普诺夫方程。应用这些方法，提出了最小极大鲁棒局部，ICI和快速ICI融合卡尔曼估计器（预测器，滤波器和平滑器），从而对于所有可允许的不确定性，保证了它们的实际估计误差方差具有相应的最小上限。还证明了它们的鲁棒性，准确性关系和收敛性。提出的ICI融合器提高了鲁棒性，并克服了原始协方差相交融合器的缺点，因此，假定鲁棒性局部估计量及其保守方差是已知的，和它们保守的协方差被忽略。应用于海上平台系统的两个仿真示例证明了它们的正确性，有效性和适用性。