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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/26798

Title: Identification of continuous-time errors-in-variables models
Author/Creator: Mahata, Kaushik; Garnier, Hugues
Description: A novel direct approach for identifying continuous-time linear dynamic errors-in-variables models is presented in this paper. The effects of the noise on the state-variable filter outputs are analyzed. Subsequently, a few algorithms to obtain consistent continuous-time parameter estimates in the errors-in-variables framework are derived. It is also possible to design search-free algorithms within our framework. The algorithms can be used for non-uniformly sampled data. The asymptotic distributions of the estimates are derived. The performances of the proposed algorithms are illustrated with some numerical simulation examples.
Relation: Automatica Vol. 42, Issue 9, p. 1477-1490
Publisher Link: http://dx.doi.org/10.1016/j.automatica.2006.04.012
Date: 2006
Publisher: Elsevier
Keyword(s): errors-in-variables; system identification; continuous-time models; linear estimation
Resource Type: journal article
Identifier: http://hdl.handle.net/1959.13/26798
Identifier: ISSN:0005-1098

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