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• W3044183493 startingPage "109091" @default.
• W3044183493 abstract "This paper studies the problem of distributed weighted least-squares (WLS) estimation for an interconnected linear measurement network with additive noise. Two types of measurements are considered: self measurements for individual nodes, and edge measurements for the connecting nodes. Each node in the network carries out distributed estimation by using its own measurement and information transmitted from its neighbours. We study two distributed estimation algorithms: a recently proposed distributed WLS algorithm and the so-called Gaussian Belief Propagation (BP) algorithm. We first establish the equivalence of the two algorithms. We then prove a key result which shows that the information matrix is always generalized diagonally dominant, under some very mild condition. Using these two results and some known convergence properties of the Gaussian BP algorithm, we show that the aforementioned distributed WLS algorithm computes the exact WLS solution asymptotically. A bound on its convergence rate is also presented." @default.
• W3044183493 created "2020-07-29" @default.
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• W3044183493 date "2020-10-01" @default.
• W3044183493 modified "2023-10-13" @default.
• W3044183493 title "Distributed weighted least-squares estimation for networked systems with edge measurements" @default.
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• W3044183493 doi "https://doi.org/10.1016/j.automatica.2020.109091" @default.
• W3044183493 hasPublicationYear "2020" @default.
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