Distributed robust estimation over randomly switching networks using $H_\infty$ consensus
For control systems engineers, this work provides a theoretical framework for distributed estimation in networks with random topology changes, though it is incremental over existing H∞ consensus methods.
The paper addresses distributed robust estimation over networks with Markovian switching topologies, proposing sufficient conditions for suboptimal H∞ consensus performance. The conditions enable gain computation via LMIs with global or local topology knowledge, and necessary conditions link graph Laplacian properties to mean-square detectability.
The paper considers a distributed robust estimation problem over a network with Markovian randomly varying topology. The objective is to deal with network variations locally, by switching observer gains at affected nodes only. We propose sufficient conditions which guarantee a suboptimal $H_\infty$ level of relative disagreement of estimates in such observer networks. When the status of the network is known globally, these sufficient conditions enable the network gains to be computed by solving certain LMIs. When the nodes are to rely on a locally available information about the network topology, additional rank constraints are used to condition the gains, given this information. The results are complemented by necessary conditions which relate properties of the interconnection graph Laplacian to the mean-square detectability of the plant through measurement and interconnection channels.