Frequency domain subspace-based identification of discrete-time power spectra from nonuniformly spaced measurements

Akcay,; Türkay, Semiha

doi:10.1109/cdc.2004.1428784

Cited by 2 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Problem that a parametric model is identifyed from the frequency domain data often arises in some applications [1,2,3]. Hence, in this paper, the objective is to formulate a prefiltering method of the frequency domain data that is used to identify the linear timeinvariant MIMO systems as a linear state space model.…”

Section: Introductionmentioning

confidence: 99%

Prefiltering of Frequency Domain Data Based on Wold's Decomposition for System Identification

Matsubara¹

2012

Stochastic Systems Theory and its Applications (SSS)

View full text Add to dashboard Cite

In this paper, we propose a prefiltering method of frequency domain data for linear multiple-input multipleoutput time-invariant system identification. This paper only deals with the case where the frequency domain input-output data is given by discrete Fourier transform. The concept of the proposed method is to eliminate the disturbance factor from the output data, it can be performed by orthogonal decomposition based on Wold's decomposition. Thus, by employing MIMO system identification method based on the frequency domain data given by the discrete Fourier transform, the data implemented by the proposed method can derive more accurate model. LQ decomposition is the key tool in the proposed method, it actually computes the orthogonal decomposition of the frequency domain data instead of Wold's decomposition. The efficacy of the proposed method is shown in numerical experimental.

show abstract

Section: Introductionmentioning

confidence: 99%

Prefiltering of Frequency Domain Data Based on Wold's Decomposition for System Identification

Matsubara¹

2012

Stochastic Systems Theory and its Applications (SSS)

View full text Add to dashboard Cite

show abstract

“…Discussion of parametric as well as nonparametric methods that mostly use time-domain data can be found in the books [1][2][3]. More recently [4,5], parametric, but non-iterative subspace-based identification algorithm have been proposed. The algorithm in [4] identifies discrete-time spectra and requires the frequencies be uniformly spaced while the algorithm in [5] does not require the discrete-frequencies be uniformly spaced.…”

Section: Introductionmentioning

confidence: 99%

“…More recently [4,5], parametric, but non-iterative subspace-based identification algorithm have been proposed. The algorithm in [4] identifies discrete-time spectra and requires the frequencies be uniformly spaced while the algorithm in [5] does not require the discrete-frequencies be uniformly spaced. A non-parametric approach would typically be based on the Fourier series development of the power spectrum [6] following a transformation of the estimation problem from the continuous-time to the discrete-time.…”

Section: Introductionmentioning

confidence: 99%