Distributed Fusion Filtering in Networked Systems with Random Measurement Matrices and Correlated Noises
Metadatos
Mostrar el registro completo del ítemEditorial
Hindawi
Materia
Distributed fusion filter Random measurement matrices Correlated noises Least-squares estimation
Fecha
2015Referencia bibliográfica
Caballero-Águila, R., García-Garrido, I., Linares-Pérez, J., (2015), Distributed Fusion Filtering in Networked Systems with Random Measurement Matrices and Correlated Noises, Discrete Dynamics in Nature and Society, Article ID 398605, 10 pages, DOI: https://doi.org/10.1155/2015/398605
Patrocinador
Ministerio de Economía y Competitividad (Grant no.MTM2014-52291-P and FPU programme)Resumen
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 sensormeasurement 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 formthe 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.