Distributed fusion filtering in networked systems with random measurement matrices and correlated noises
type: Item
instance of: scholarly article
title: Distributed fusion filtering in networked systems with random measurement matrices and correlated noises
zbMATH Open document ID: 1418.93262
DOI: 10.1155/2015/398605
author: I. García-Garrido
author: R. Caballero-Águila
author: Josefa Linares-Pérez
published in: Discrete Dynamics in Nature and Society
publication date: 2019-02-19T00:00:00Z
review text: Summary: The distributed fusion state estimation problem is addressed for sensor network systems with random state transition matrix and random measurement matrices, which provide a unified framework to consider some network-induced random phenomena. The process noise and all the sensor measurement noises are assumed to be 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. These correlation assumptions cover many practical situations, where the classical independence hypothesis is not realistic. Using an innovation methodology, local least-squares linear filtering estimators are recursively obtained at each sensor. The distributed fusion method is then used to form the optimal matrix-weighted sum of these local filters according to the mean squared error criterion. A numerical simulation example shows the accuracy of the proposed distributed fusion filtering algorithm and illustrates some of the network-induced stochastic uncertainties that can be dealt with in the current system model, such as sensor gain degradation, missing measurements, and multiplicative noise.
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 65C60
Mathematics Subject Classification ID: 93C55
zbMATH DE Number: 7025343
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/398605
OpenAlex ID: W2291858712
Wikidata QID: Q59105870
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description: scientific article; zbMATH DE number 7025343