Design of distributed decentralized estimators for formations with fixed and stochastic communication topologies

Distributed H<inf>&#x221E;</inf> filtering over randomly switching networks

“…Unless the governing Markov process is composed from independent two-state Markov processes describing the status of individual links [6], [12], [15], the majority of references employing Markovian network models rely on the assumption that the complete state of the underlying Markov chain is known to every controller or filter [7], [1]. In the context of distributed filtering, this assumption requires each node of the network to know the graph of the entire network in order to deploy suitable gains.…”

“…Recently, there has been a growing interest in Markovian switching models to describe estimator networks, where the probability of communication links to be active is governed by a Markovian switching rule [15], [12], [8]. Such models are widely used in the analysis of communication channels with random data loss.…”

A linear distributed filter inspired by the Markovian jump linear system filtering problem

“…Most papers focusing on distributed estimation propose different mechanisms for combining the standard Kalman filter with a consensus filter in order to ensure that the estimates asymptotically converge to the same value [2,[5][6][7]. More recent contributions on the design of distributed decentralized estimators can be found in [8]. Email addresses: imatei@umd.edu (Ion Matei), baras@umd.edu (John S. Baras).…”

An Extension to Balanced Truncation With Application to Structured Model Reduction

“…In [15], [16] the applications were stability analysis and controller synthesis for parameter-dependent models. The same type of extension was also used in [17] In [18], the extension was first used for model reduction. In this paper, more general results showing how extended Gramians can be used for model comparison, and a new application on structured model reduction are presented.…”