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
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
to 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.
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