Distributed H∞ filtering over randomly switching networks

The paper considers a distributed robust estimation problem over a network with randomly changing topology. The objective is to deal with these changes locally, by switching observer gains at affected nodes only. We propose sufficient conditions which guarantee a suboptimal H_∞ level of disagreement of estimates in such observer networks, both in the mean-square sense and with probability 1. 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.