Optimized Selective Activations for Networked Filtering

This article studies distributed state estimation for a dynamic system based on measurements collected by every node in a sensor network. Depending on the communications among nodes, information spreads across the network node by node through iterations. Then, nodes work collaboratively to estimate the system state timely. Most of the existing networked filters aim to make all nodes reach consensus at the centralized estimation with sufficient communications. To accommodate limited communication resources in practice, this article proposes to optimize the entire network's estimation accuracy given in a fixed number of communications. This is done by optimizing the sequence of node activations for selective communication. We propose two selective activation schemes: link activation (LA) and star activation (SA). In each iteration, they activate a single link and a single star (i.e., a node with all its neighbors), respectively. We develop two iterative distributed filters (DFs): 1) LA based (LA-DF); and 2) SA based (SA-DF). LA-DF and SA-DF possess many important properties. For example, they are unbiased, convergent, stable, and credible. Finally, we analyze the performance of our filters and provide simulation results compared with existing filters to verify the superiority of the proposed filters.