Distributed fusion filtering for multi-sensor systems with correlated random transition and measurement matrices

ABSTRACT

This paper is concerned with the distributed fusion estimation problem for discrete-time linear stochastic systems with measurements coming from different sensors and correlated random parameter matrices in both the state and measurement equations. At each sampling time, the random state transition parameter matrices are assumed to be correlated at the same sampling time with the measurement random parameter matrices of each sensor. Moreover, the random parameter matrices in the observation equations are one-step auto-correlated and cross-correlated between the different sensors. The additive noises are also assumed to be correlated. Under these assumptions, the distributed fusion filter is designed as the matrix-weighted linear combination of the local least-squares linear filters obtained at every single sensor, using the linear minimum variance optimality criterion. A numerical simulation example considering a two-sensor system with randomly delayed measurements is used to illustrate the applicability of multi-sensor systems with correlated random parameter matrices and analyse the performance of the proposed filtering estimators.

KEYWORDS:

• Distributed fusion filter
• multi-sensor systems
• random parameter matrices
• correlated noises
• random delays

AMS 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, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional (FEDER) (grant no. MTM2017-84199-P).