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Frequency-Domain Identification of Continuous-Time ARMA Models from Sampled Data
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2009 (English)In: Automatica, ISSN 0005-1098, Vol. 45, no 6, 1371-1378 p.Article in journal (Refereed) Published
Abstract [en]

The subject of this paper is the direct identification of continuous-time autoregressive moving average (CARMA) models. The topic is viewed from the frequency domain perspective which then turns the reconstruction of the continuous-time power spectral density (CT-PSD) into a key issue. The first part of the paper therefore concerns the approximate estimation of the CT-PSD from uniformly sampled data under the assumption that the model has a certain relative degree. The approach has its point of origin in the frequency domain Whittle likelihood estimator. The discrete- or continuous-time spectral densities are estimated from equidistant samples of the output. For low sampling rates the discrete-time spectral density is modeled directly by its continuous-time spectral density using the Poisson summation formula. In the case of rapid sampling the continuous-time spectral density is estimated directly by modifying its discrete-time counterpart.

Place, publisher, year, edition, pages
Elsevier, 2009. Vol. 45, no 6, 1371-1378 p.
Keyword [en]
System identification, Time-series analysis, Frequency domains, Continuous time systems, ARMA parameter estimation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-19676DOI: 10.1016/j.automatica.2009.01.016OAI: oai:DiVA.org:liu-19676DiVA: diva2:227304
Available from: 2009-07-10 Created: 2009-07-10 Last updated: 2013-09-23

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Ljung, Lennart

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