Distributed state estimation with compressed and synchronized auxiliary particle filters using graph theory
Iman Maghsudlu,
M. R. Danaee,
Hamid Arezumand
Abstract:In this paper, we propose a novel compressed distributed auxiliary particle filter that uses graph theory (CDAPF-GT) to reduce the communication cost and improve the estimation accuracy of a nonlinear state space model. Our method compresses the global loglikelihood function into a set of parameters that are updated by an average consensus algorithm over a network of nodes. Unlike the existing methods, our method synchronizes the particle sets among all the nodes and uses the latest measurements to construct a… Show more
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