Distributed robust estimation over randomly switching networks using $H_{\infty }$ consensus

Abstract

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.

Cited by in corpus (6)

• Distributed Kalman Filter in a Network of Linear Dynamical Systems
• Cooperative H-infinity Estimation for Large-Scale Interconnected Linear Systems
• Robust Detection of Biasing Attacks on Misappropriated Distributed Observers via Decentralized $H_{\infty }$ synthesis
• H2 and H-infinity Suboptimal Distributed Filter Design for Linear Systems
• Decentralized Cooperative Online Estimation With Random Observation Matrices, Communication Graphs and Time Delays
• Cooperative Robust Estimation with Local Performance Guarantees