Covariance-Based Estimation from Multisensor Delayed Measurements with Random Parameter Matrices and Correlated Noises Caballero-Águila, R. Hermoso-Carazo, Aurora Linares-Pérez, Josefa Análisis de covarianza Analysis of covariance Funciones recursivas Recursive functions Detectores Detectors Algoritmos Algorithms 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. 2015-03-18T13:43:15Z 2015-03-18T13:43:15Z 2014 info:eu-repo/semantics/article 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] 1024-123X 1563-5147 http://hdl.handle.net/10481/35298 10.1155/2014/958474 eng info:eu-repo/semantics/openAccess Hindawi Publishing Corporation