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On the relation between weighted frequency-domain maximum-likelihood power spectral estimation and the prefiltered covariance extension approach
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification Group)ORCID iD: 0000-0002-1927-1690
2007 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 55, no 1, 384-389 p.Article in journal (Refereed) Published
Abstract [en]

The aim of this correspondence is to study the connection between weighted frequency-domain maximum-likelihood power spectral estimation and the time-domain prefiltered covariance extension approach. Weighting and prefiltering are introduced to emphasize the model fit in a certain frequency range. The main result is that these two methods are very closely related for the case of autoregressive (AR) model estimation, which implies that both can be formulated as convex optimization problems. Examples illustrating the methods and the effect of prefiltering/weighting are provided.

Place, publisher, year, edition, pages
2007. Vol. 55, no 1, 384-389 p.
Keyword [en]
autoregressive (AR) processes, covariance analysis, frequency-domain, analysis, spectral analysis, convex-optimization approach, time-series
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-16308DOI: 10.1109/tsp.2006.885780ISI: 000243281200035Scopus ID: 2-s2.0-33947425607OAI: oai:DiVA.org:kth-16308DiVA: diva2:334350
Note
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2017-12-12Bibliographically approved

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Wahlberg, Bo

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