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

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.

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