Dual time-frequency domain system identification

Authors:
Juan C. AgüEro;Wei Tang;Juan I. Yuz;RamóN Delgado;Graham C. Goodwin
Affiliations:
School of Electrical Engineering and Computer Science, The University of Newcastle, Australia;School of Automation, Northwestern Polytechnical University, China;Electronic Engineering Department, Universidad Técnica Federico Santa María, Chile;School of Electrical Engineering and Computer Science, The University of Newcastle, Australia;School of Electrical Engineering and Computer Science, The University of Newcastle, Australia
Venue:
Automatica (Journal of IFAC)
Year:
2012

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Abstract

In this paper we obtain the maximum likelihood estimate of the parameters of discrete-time linear models by using a dual time-frequency domain approach. We propose a formulation that considers a (reduced-rank) linear transformation of the available data. Such a transformation may correspond to different options: selection of time-domain data, transformation to the frequency domain, or selection of frequency-domain data obtained from time-domain samples. We use the proposed approach to identify multivariate systems represented in state-space form by using the Expectation-Maximisation algorithm. We illustrate the benefits of the approach via numerical examples.