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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/35298

Title: Covariance-Based Estimation from Multisensor Delayed Measurements with Random Parameter Matrices and Correlated Noises
Authors: Caballero-Águila, R.
Hermoso-Carazo, Aurora
Linares-Pérez, Josefa
Issue Date: 2014
Abstract: The optimal least-squares linear estimation problem is addressed for a class of discrete-time multisensor linear stochastic systems subject to randomly delayed measurements with different delay rates. For each sensor, a different binary sequence is used to model the delay process. The measured outputs are perturbed by both random parameter matrices and one-step autocorrelated and cross correlated noises. Using an innovation approach, computationally simple recursive algorithms are obtained for the prediction, filtering, and smoothing problems, without requiring full knowledge of the state-space model generating the signal process, but only the information provided by the delay probabilities and the mean and covariance functions of the processes (signal, random parameter matrices, and noises) involved in the observation model. The accuracy of the estimators is measured by their error covariance matrices, which allow us to analyze the estimator performance in a numerical simulation example that illustrates the feasibility of the proposed algorithms.
Publisher: Hindawi Publishing Corporation
Keywords: Análisis de covarianza
Analysis of covariance
Funciones recursivas
Recursive functions
Detectores
Detectors
Algoritmos
Algorithms
URI: http://hdl.handle.net/10481/35298
ISSN: 1024-123X
1563-5147
Citation: Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J. Covariance-Based Estimation from Multisensor Delayed Measurements with Random Parameter Matrices and Correlated Noises. Mathematical Problems in Engineering, 2014: 958474 (2014). [http://hdl.handle.net/10481/35298]
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