Distributed Fusion Filtering in Networked Systems with Random Measurement Matrices and Correlated Noises

Raquel Caballero-Águila, Irene García-Garrido and Josefa Linares-Pérez

Discrete Dynamics in Nature and Society, 2015, vol. 2015, 1-10

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:398605

DOI: 10.1155/2015/398605

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