This paper is concerned with the distributed ${H_\infty }$ state estimation for a discrete-time target linear system over a filtering network with time-varying and switching topology and partial information exchange. Both filtering network topology switching and partial information exchange between filters are simultaneously considered in the filter design. The topology under consideration evolves not only over time but also by an event switch which is assumed to be subject to a nonhomogeneous Markov chain. The probability transition matrix of the nonhomogeneous Markov chain is time-varying. In the filter information exchange, partial state estimation information and channel noise are simultaneously considered. In order to design such a switching filtering network with partial information exchange, stochastic Markov stability theory is developed. The switching topology-dependent filters are derived to guarantee an optimal ${H_{\infty }}$ disturbance rejection attenuation level for the estimation disagreement of the filtering network. It is shown that the addressed ${H_\infty }$ state estimation problem is turned into a switching topology-dependent optimal problem. The distributed filtering problem with complete information exchanges from its neighbors is also investigated. An illustrative example is given to show the applicability of the obtained results.

中文翻译：

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具有时变和交换拓扑以及部分信息交换的滤波网络上的分布式H∞状态估计

本文涉及分布式 $ {H_ \ infty} $ 具有时变和切换拓扑以及部分信息交换的离散时间目标线性系统在滤波网络上的状态估计。过滤器设计中同时考虑了过滤网络拓扑切换和过滤器之间的部分信息交换。所考虑的拓扑结构不仅会随着时间的推移而发展，而且还会通过事件切换而发生变化，该事件切换被认为会受到影响。非齐次马尔可夫链。的概率转移矩阵不均匀的 马尔可夫链是 随时间变化。在滤波器信息交换中，同时考虑部分状态估计信息和信道噪声。为了设计这种具有部分信息交换的交换滤波网络，发展了随机马尔可夫稳定性理论。派生出与开关拓扑相关的滤波器，以确保获得最佳的 $ {H _ {\ infty}} $ 干扰抑制衰减水平，用于估计滤波网络的不一致性。显示出已解决 $ {H_ \ infty} $ 状态估计问题变成与开关拓扑有关的最优问题。还研究了来自邻居的具有完整信息交换的分布式过滤问题。给出一个说明性的例子来说明所获得的结果的适用性。