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Variance analysis of linear SIMO models with spatially correlated noise
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-1127-1397
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-0355-2663
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9368-3079
2017 (English)In: Automatica, ISSN 0005-1098, Vol. 77, p. 68-81Article in journal (Refereed) Published
Abstract [en]

In this paper we address the identification of linear time-invariant single-input multi-output (SIMO) systems. In particular, we assess the performance of the prediction error method by quantifying the variance of the parameter estimates. Using an orthonormal representation for the modules composing the SIMO structure, we show that the parameter estimate of a module depends on the model structure of the other modules, and on the correlation structure of the output disturbances. We provide novel results which quantify the variance-error of the parameter estimates for finite model orders, where the effects of noise correlation structure, model structure and input spectrum are visible. In particular, we show that a sensor does not increase the accuracy of a module if common dynamics have to be estimated. When a module is identified using less parameters than the other modules, we derive the noise correlation structure that gives the minimum total variance. The implications of our results are illustrated through numerical examples and simulations.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 77, p. 68-81
Keywords [en]
Asymptotic variance, Least-squares, Linear SIMO models, System identification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-200878DOI: 10.1016/j.automatica.2016.11.017Scopus ID: 2-s2.0-85009201666OAI: oai:DiVA.org:kth-200878DiVA, id: diva2:1071098
Note

QC 20170203

Available from: 2017-02-03 Created: 2017-02-03 Last updated: 2017-02-03Bibliographically approved

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Everitt, NiklasBottegal, GiulioRojas, Cristian R.Hjalmarsson, Håkan
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