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On frequency weighting in autoregressive spectral estimation
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
KTH, School of Electrical Engineering (EES), Automatic Control. (System Identification Group)ORCID iD: 0000-0002-1927-1690
2005 (English)In: IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2005, 245-248 p.Conference paper (Refereed)
Abstract [en]

This paper treats the problem of approximating a complex stochastic process in a given frequency region by an estimated autoregressive (AR) model. Two frequency domain approaches are discussed: a weighted frequency domain maximum likelihood method and a prefiltered covariance extension method based on the theory of Lindquist and co-workers. It is shown that these two approaches are very closely related and can both be formulated as convex optimization problems. An examples illustrating the methods and the effect of prefiltering/weighting is provided. The results show that these methods are capable of tuning the AR model fit to a specified frequency region.

Place, publisher, year, edition, pages
2005. 245-248 p.
, International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
National Category
Signal Processing Control Engineering
URN: urn:nbn:se:kth:diva-26580DOI: 10.1109/ICASSP.2005.1415991ISI: 000229404203062ScopusID: 2-s2.0-33646807509ISBN: 0-7803-8874-7OAI: diva2:379145
30th IEEE International Conference on Acoustics, Speech, and Signal Processing Philadelphia, PA, MAR 19-23, 2005
Swedish Research Council

QC 20101217

Available from: 2010-12-17 Created: 2010-11-25 Last updated: 2016-06-22Bibliographically approved

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