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Optimal enforcement of causality in non-parametric transfer function estimation
2017 (English)In: IEEE Control Systems Letters, ISSN 2475-1456, Vol. 1, no 2, p. 268-273Article in journal (Refereed) Published
Abstract [en]

Traditionally, non-parametric impulse and frequency response functions are estimated by taking the ratio of power spectral density estimates. However, this approach may often lead to non-causal estimates. In this letter, we derive a closed form expression for the impulse response estimator by smoothed empirical transfer function estimate, which allows optimal enforcement of causality on non-parametric estimators based on spectral analysis. The new method is shown to be asymptotically unbiased and of minimum covariance in a positive semidefinite sense among a broad class of linear estimators. Numerical simulations illustrate the performance of the new estimator. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2017. Vol. 1, no 2, p. 268-273
Keywords [en]
Causality, ETFE, Non-parametric estimation, Spectral analysis, System identification, Frequency response, Identification (control systems), Impulse response, Spectral density, Spectrum analysis, Transfer functions, Closed-form expression, Empirical transfer function estimates, Frequency response functions, Non-parametric estimations, Positive semidefinite, Transfer function estimation, Parameter estimation
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-246927DOI: 10.1109/LCSYS.2017.2713821Scopus ID: 2-s2.0-85057215073OAI: oai:DiVA.org:kth-246927DiVA, id: diva2:1327198
Note

QC 20190619

Available from: 2019-06-19 Created: 2019-06-19 Last updated: 2019-06-19Bibliographically approved

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