Distributed fusion estimation from measurements with correlated random parameter matrices and noise correlation

ABSTRACT

This paper addresses the distributed fusion estimation problem for discrete-time multi-sensor stochastic systems with random parameter matrices. It is assumed that the random parameter matrices in the observation equations are one-step autocorrelated and cross-correlated between the different sensors and the additive noises are also correlated. Under these assumptions, a recursive algorithm is proposed to obtain local least squares linear filters based on the measurements of each sensor, and the distributed fusion filter is designed as the matrix-weighted linear combination of these estimators which minimizes the mean squared estimation error. This research is illustrated by two numerical simulation examples where multi-sensor systems with randomly delayed measurements and missing measurements are considered, respectively, and the performance of the proposed estimators is analysed by comparing the estimation error variances of the distributed and centralized fusion filters.

KEYWORDS:

• Distributed fusion filter
• random parameter matrices
• noise correlation
• random delays
• missing measurements

2010 AMS SUBJECT CLASSIFICATIONS:

• 60G35
• 62M20
• 93E11

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Raquel Caballero-Águila  http://orcid.org/0000-0001-7659-7649

Irene García-Garrido  http://orcid.org/0000-0003-3101-4088

Josefa Linares-Pérez  http://orcid.org/0000-0002-6853-555X

Additional information

Funding

This work is supported by Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional FEDER [grant nos. MTM2014-52291-P and MTM2017-84199-P].