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Authors: R. Caballero-Águila 1 ; A. Hermoso-Carazo 2 and J. Linares-Pérez 2

Affiliations: 1 Departamento de Estadística e I.O., Universidad de Jaén, Campus Las Lagunillas s/n, 23071 Jaén and Spain ; 2 Departamento de Estadística e I.O., Universidad de Granada, Campus Fuentenueva s/n, 18071 Granada and Spain

ISBN: 978-989-758-380-3

Keyword(s): Least-squares Estimation Algorithms, Filtering, Fixed-point Smoothing, Covariance Information, Random Parameter Matrices, Time-correlated Noise.

Related Ontology Subjects/Areas/Topics: Informatics in Control, Automation and Robotics ; Signal Processing, Sensors, Systems Modeling and Control ; System Modeling

Abstract: This paper is concerned with the least-squares linear estimation problem for a class of discrete-time networked systems whose measurements are perturbed by random parameter matrices and time-correlated additive noise, without requiring a full knowledge of the state-space model generating the signal process, but only information about its mean and covariance functions. Assuming that the measurement additive noise is the output of a known linear system driven by white noise, the time-differencing method is used to remove this time-correlated noise and recursive algorithms for the linear filtering and fixed-point smoothing estimators are obtained by an innovation approach. These estimators are optimal in the least-squares sense and, consequently, their accuracy is evaluated by the estimation error covariance matrices, for which recursive formulas are also deduced. The proposed algorithms are easily implementable, as it is shown in the computer simulation example, where they are applied t o estimate a signal from measured outputs which, besides including time-correlated additive noise, are affected by the missing measurement phenomenon and multiplicative noise (random uncertainties that can be covered by the current model with random parameter matrices). The computer simulations also illustrate the behaviour of the filtering estimators for different values of the missing measurement probability. (More)

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Paper citation in several formats:
Caballero-Águila, R.; Hermoso-Carazo, A. and Linares-Pérez, J. (2019). Signal Estimation with Random Parameter Matrices and Time-correlated Measurement Noises.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 481-487. DOI: 10.5220/0007807804810487

@conference{icinco19,
author={Caballero{-}Águila, R. and A. Hermoso{-}Carazo. and J. Linares{-}Pérez.},
title={Signal Estimation with Random Parameter Matrices and Time-correlated Measurement Noises},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={481-487},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007807804810487},
isbn={978-989-758-380-3},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Signal Estimation with Random Parameter Matrices and Time-correlated Measurement Noises
SN - 978-989-758-380-3
AU - Caballero-Águila, R.
AU - Hermoso-Carazo, A.
AU - Linares-Pérez, J.
PY - 2019
SP - 481
EP - 487
DO - 10.5220/0007807804810487

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