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dc.contributor.advisorBaras, John
dc.contributor.authorMatei, Ion
dc.contributor.authorBaras, John
dc.date.accessioned2010-03-11T15:46:22Z
dc.date.available2010-03-11T15:46:22Z
dc.date.issued2010-03
dc.identifier.urihttp://hdl.handle.net/1903/10047
dc.description.abstractWe address the consensus-based distributed linear filtering problem, where a discrete time, linear stochastic process is observed by a network of sensors. We assume that the consensus weights are known and we first provide sufficient conditions under which the stochastic process is detectable, i.e. for a specific choice of consensus weights there exists a set of filtering gains such that the dynamics of the estimation errors (without noise) is asymptotically stable. Next, we provide a distributed sub-optimal filtering scheme based on optimizing an upper bound on a quadratic filtering cost. In the stationary case, we provide sufficient conditions under which this scheme converges; conditions expressed in terms of the convergence properties of a set of coupled Riccati equations. We continue with presenting a connection between the consensus-based distributed linear filter and the optimal linear filter of a Markovian jump linear system, appropriately defined. More specifically, we show that if the Markovian jump linear system is (mean square) detectable, then the stochastic process is detectable under the consensus-based distributed linear filtering scheme. We also show that the state estimate given by the optimal linear filter of a Markovian jump linear system appropriately defined can be seen as an approximation of the optimal average state estimate obtained using the consensus-based linear filtering scheme.en_US
dc.description.sponsorshipThis material is based upon the work supported by the US Air Force Office of Scientific Research award FA9550-09-0538 to Georgia Techen_US
dc.language.isoenen_US
dc.relation.ispartofseriesTR_2010-5;
dc.subjectdistributed filteringen_US
dc.subjectconsensusen_US
dc.subjectMarkovian linear systemen_US
dc.titleConsensus-Based Distributed Filteringen_US
dc.typeArticleen_US
dc.relation.isAvailableAtInstitute for Systems Researchen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us


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