@misc{10481/67363,
year = {2019},
month = {1},
url = {http://hdl.handle.net/10481/67363},
abstract = {This paper is concerned with the distributed filtering problem for a class of discrete-time stochastic systems over
a sensor network with a given topology. The system presents the following main features: (i) random parameter
matrices in both the state and observation equations are considered; and (ii) the process and measurement noises
are one-step autocorrelated and two-step cross-correlated. The state estimation is performed in two stages. At the
first stage, through an innovation approach, intermediate distributed least-squares linear filtering estimators are
obtained at each sensor node by processing available output measurements not only from the sensor itself but
also from its neighboring sensors according to the network topology. At the second stage, noting that at each
sampling time not only the measurement but also an intermediate estimator is available at each sensor, attention
is focused on the design of distributed filtering estimators as the least-squares matrix-weighted linear combination
of the intermediate estimators within its neighborhood. The accuracy of both intermediate and distributed
estimators, which is measured by the error covariance matrices, is examined by a numerical simulation
example where a four-sensor network is considered. The example illustrates the applicability of the proposed
results to a linear networked system with state-dependent multiplicative noise and different network-induced
stochastic uncertainties in the measurements; more specifically, sensor gain degradation, missing measurements
and multiplicative observation noises are considered as particular cases of the proposed observation model.},
organization = {This research is supported by Ministerio de Economía y Competitividad
and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2014-
52291-P, MTM2017-84199-P).},
publisher = {Elsevier},
keywords = {Sensor Networks},
keywords = {Distributed filtering},
keywords = {Random Parameter Matrices},
keywords = {Correlated noises},
title = {A new approach to distributed fusion filtering for networked systems with random parameter matrices and correlated noises},
doi = {https://doi.org/10.1016/j.inffus.2018.02.006},
author = {Caballero-Águila, Raquel and Hermoso Carazo, Aurora and Linares Pérez, Josefa},
}