TY - JOUR
A2 - Ding, Xiaohua
AU - Caballero-Águila, Raquel
AU - García-Garrido, Irene
AU - Linares-Pérez, Josefa
PY - 2015
DA - 2015/12/09
TI - Distributed Fusion Filtering in Networked Systems with Random Measurement Matrices and Correlated Noises
SP - 398605
VL - 2015
AB - The distributed fusion state estimation problem is addressed for sensor network systems with random state transition matrix and random measurement matrices, which provide a unified framework to consider some network-induced random phenomena. The process noise and all the sensor measurement noises are assumed to be one-step autocorrelated and different sensor noises are one-step cross-correlated; also, the process noise and each sensor measurement noise are two-step cross-correlated. These correlation assumptions cover many practical situations, where the classical independence hypothesis is not realistic. Using an innovation methodology, local least-squares linear filtering estimators are recursively obtained at each sensor. The distributed fusion method is then used to form the optimal matrix-weighted sum of these local filters according to the mean squared error criterion. A numerical simulation example shows the accuracy of the proposed distributed fusion filtering algorithm and illustrates some of the network-induced stochastic uncertainties that can be dealt with in the current system model, such as sensor gain degradation, missing measurements, and multiplicative noise.
SN - 1026-0226
UR - https://doi.org/10.1155/2015/398605
DO - 10.1155/2015/398605
JF - Discrete Dynamics in Nature and Society
PB - Hindawi Publishing Corporation
KW -
ER -