Identification of state-space systems using a dual time-frequency domain approach

Author

Agüero, Juan C. ; Yuz, Juan I. ; Goodwin, Graham C. ; Tang, Wei

fYear

2010

fDate

15-17 Dec. 2010

Firstpage

2863

Lastpage

2868

Abstract

In this paper we obtain the maximum likelihood estimate of the parameters of discrete-time state-space models by using a dual time-frequency domain approach. We propose an Expectation Maximization formulation that considers a (non-bijective) 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 also explore the application of these ideas to Errors-In-Variables systems.

Keywords

discrete time systems; expectation-maximisation algorithm; stochastic systems; time-frequency analysis; discrete-time state-space models; dual time-frequency domain approach; errors-in-variables system; expectation maximization formulation; frequency-domain data; maximum likelihood estimate; nonbijective linear data transformation; parameter estimation; state-space system identification; stochastic system; time-domain data; Frequency estimation; Matrix decomposition; Maximum likelihood estimation; Noise; Time frequency analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control (CDC), 2010 49th IEEE Conference on

Conference_Location

Atlanta, GA

ISSN

0743-1546

Print_ISBN

978-1-4244-7745-6

Type

conf

DOI

10.1109/CDC.2010.5717056

Filename

5717056