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Optimal state estimation for networked systems with random parameter matrices,correlated noises and delayed measurements
Authors:R. Caballero-Águila  A. Hermoso-Carazo
Affiliation:1. Department of Statistics and Operations Research, University of Jaén, Jaén, Spain.;2. Department of Statistics and Operations Research, University of Granada, Granada, Spain.
Abstract:In this paper, the optimal least-squares state estimation problem is addressed for a class of discrete-time multisensor linear stochastic systems with state transition and measurement random parameter matrices and correlated noises. It is assumed that at any sampling time, as a consequence of possible failures during the transmission process, one-step delays with different delay characteristics may occur randomly in the received measurements. The random delay phenomenon is modelled by using a different sequence of Bernoulli random variables in each sensor. The process noise and all the sensor measurement noises are one-step autocorrelated and different sensor noises are one-step cross-correlated. Also, the process noise and each sensor measurement noise are two-step cross-correlated. Based on the proposed model and using an innovation approach, the optimal linear filter is designed by a recursive algorithm which is very simple computationally and suitable for online applications. A numerical simulation is exploited to illustrate the feasibility of the proposed filtering algorithm.
Keywords:optimal least-squares estimation  random parameter matrices  correlated noises  randomly delayed observations  innovation approach
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