distributed fusion filtering in networked systems with random measurement matrices and correlated noises
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2015
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Abstract
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.
| Reference Key |
caballero-guila2015discretedistributed
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|---|---|
| Authors | ;Raquel Caballero-Águila;Irene García-Garrido;Josefa Linares-Pérez |
| Journal | Journal of the American Heart Association |
| Year | 2015 |
| DOI |
10.1155/2015/398605
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| URL | |
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