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Sensors 2016, 16(6), 847; doi:10.3390/s16060847

Networked Fusion Filtering from Outputs with Stochastic Uncertainties and Correlated Random Transmission Delays

1
Departamento de Estadística, Universidad de Jaén, Campus Las Lagunillas, 23071 Jaén, Spain
2
Departamento de Estadística, Universidad de Granada, Avda. Fuentenueva, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Xue-Bo Jin
Received: 5 May 2016 / Revised: 31 May 2016 / Accepted: 3 June 2016 / Published: 8 June 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
View Full-Text   |   Download PDF [368 KB, uploaded 8 June 2016]   |  

Abstract

This paper is concerned with the distributed and centralized fusion filtering problems in sensor networked systems with random one-step delays in transmissions. The delays are described by Bernoulli variables correlated at consecutive sampling times, with different characteristics at each sensor. The measured outputs are subject to uncertainties modeled by random parameter matrices, thus providing a unified framework to describe a wide variety of network-induced phenomena; moreover, the additive noises are assumed to be one-step autocorrelated and cross-correlated. Under these conditions, without requiring the knowledge of the signal evolution model, but using only the first and second order moments of the processes involved in the observation model, recursive algorithms for the optimal linear distributed and centralized filters under the least-squares criterion are derived by an innovation approach. Firstly, local estimators based on the measurements received from each sensor are obtained and, after that, the distributed fusion filter is generated as the least-squares matrix-weighted linear combination of the local estimators. Also, a recursive algorithm for the optimal linear centralized filter is proposed. In order to compare the estimators performance, recursive formulas for the error covariance matrices are derived in all the algorithms. The effects of the delays in the filters accuracy are analyzed in a numerical example which also illustrates how some usual network-induced uncertainties can be dealt with using the current observation model described by random matrices. View Full-Text
Keywords: least-squares estimation; distributed and centralized fusion methods; random parameter matrices; correlated noises; random delays least-squares estimation; distributed and centralized fusion methods; random parameter matrices; correlated noises; random delays
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J. Networked Fusion Filtering from Outputs with Stochastic Uncertainties and Correlated Random Transmission Delays. Sensors 2016, 16, 847.

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