@misc{10481/67666,
year = {2020},
month = {1},
url = {http://hdl.handle.net/10481/67666},
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.},
organization = {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).},
publisher = {Taylor&Francis},
keywords = {Distributed fusion filter},
keywords = {Multi-sensor systems},
keywords = {Random parameter matrices},
keywords = {Correlated noises},
keywords = {Random delays},
title = {Distributed fusion filtering for multi-sensor systems with correlated random transition and measurement matrices},
doi = {https://doi.org/10.1080/00207160.2018.1554213},
author = {Caballero-Águila, Raquel and García Garrido, Irene and Linares Pérez, Josefa},
}