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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/26054

Title
Modeling continuous-time processes via input-to-state filters
Author/Creator
Mahata, Kaushik;  Fu, Minyue
Description
A direct algorithm to estimate continuous-time ARMA (CARMA) models is proposed in this paper. In this approach, we first pass the observed data through an input-to-state filter and compute the state covariance matrix. The properties of the state covariance matrix are then exploited to estimate the half-spectrum of the observed data at a set of user-defined points on the right-half plane. Finally, the continuous-time parameters are obtained from the half-spectrum estimates by solving an analytic interpolation problem with a positive real constraint. As shown by simulations, the proposed algorithm delivers much more reliable estimates than indirect modeling approaches, which rely on estimating an intermediate discrete-time model.
Relation
Automatica Vol. 42, Issue 7, p. 1073-1084
Publisher Link
http://dx.doi.org/10.1016/j.automatica.2006.02.014
Date
2006
Publisher
Elsevier
Keyword(s)
continuous-time processes;  identification;  input-to-state filtering;  ARMA modeling
Resource Type
journal article
Identifier
http://hdl.handle.net/1959.13/26054
Identifier
ISSN:0005-1098
Reviewed
Reviewed
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Subject
"Automatica"
 
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