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Certifying the Optimality of a Distributed State Estimation System via Majorization Theory

dc.contributor.advisorMartins, Nuno
dc.contributor.authorLipsa, Gabriel
dc.contributor.authorMartins, Nuno
dc.description.abstractConsider a first order linear time-invariant discrete time system driven by process noise, a pre-processor that accepts causal measurements of the state of the system, and a state estimator. The pre-processor and the state estimator are not co-located, and, at every time-step, the pre-processor transmits either a real number or an erasure symbol to the estimator. We seek the pre-processor and the estimator that jointly minimize a cost that combines two terms; the expected squared state estimation error and a communication cost. In our formulation, the transmission of a real number from the pre-processor to the estimator incurs a positive cost while erasures induce zero cost. This paper is the first to prove analytically that a symmetric threshold policy at the pre-processor and a Kalman-like filter at the estimator, which updates its estimate linearly in the presence of erasures, are jointly optimal for our problem.en
dc.format.extent452512 bytes
dc.titleCertifying the Optimality of a Distributed State Estimation System via Majorization Theoryen
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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