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Estimation of noise covariance matrices for periodic systems
Author(s) -
Šimandl Miroslav,
Duník Jindřich
Publication year - 2011
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.1255
Subject(s) - covariance , noise (video) , estimation of covariance matrices , covariance matrix , mathematics , covariance intersection , statistics , analysis of covariance , algorithm , state (computer science) , covariance function , computer science , artificial intelligence , image (mathematics)
Estimation of the noise covariance matrices for linear time‐variant stochastic dynamic periodic systems is treated. The novel offline method for estimation of the covariance matrices of the state and measurement noises is designed. The method is based on analysis of second‐order statistics of the state estimate produced by the linear multi‐step predictor. The estimates of the noise covariance matrices are unbiased and converge to the true values with increasing number of data. The theoretical results are illustrated in numerical examples. Copyright © 2011 John Wiley & Sons, Ltd.