Liu Yang, Wang Zidong, Zhou Donghua
IEEE Trans Neural Netw Learn Syst. 2020 Aug;31(8):2930-2941. doi: 10.1109/TNNLS.2019.2934131. Epub 2019 Sep 5.
This article is concerned with the distributed filtering problem for a class of discrete complex networks over time-varying topology described by a sequence of variables. In the developed scalable filtering algorithm, only the local information and the information from the neighboring nodes are used. As such, the proposed filter can be implemented in a truly distributed manner at each node, and it is no longer necessary to have a certain center node collecting information from all the nodes. The aim of the addressed filtering problem is to design a time-varying filter for each node such that an upper bound of the filtering error covariance is ensured and the desired filter gain is then calculated by minimizing the obtained upper bound. The filter is established by solving two sets of recursive matrix equations, and thus, the algorithm is suitable for online application. Sufficient conditions are provided under which the filtering error is exponentially bounded in mean square. The monotonicity of the filtering error with respect to the coupling strength is discussed as well. Finally, an illustrative example is presented to demonstrate the feasibility and effectiveness of our distributed filtering strategy.
本文关注一类由一系列变量描述的时变拓扑离散复杂网络的分布式滤波问题。在所提出的可扩展滤波算法中，仅使用局部信息和来自相邻节点的信息。因此，所提出的滤波器可以在每个节点以真正分布式的方式实现，不再需要某个中心节点从所有节点收集信息。所解决的滤波问题的目的是为每个节点设计一个时变滤波器，以确保滤波误差协方差的上界，然后通过最小化获得的上界来计算期望的滤波器增益。该滤波器通过求解两组递归矩阵方程来建立，因此该算法适用于在线应用。给出了滤波误差在均方意义下指数有界的充分条件。还讨论了滤波误差相对于耦合强度的单调性。最后，给出一个示例来说明我们分布式滤波策略的可行性和有效性。
