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Efficient spectral analysis in the missing data case using sparse ML methods
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.ORCID iD: 0000-0001-5158-9255
2014 (English)Conference paper (Refereed)
Abstract [en]

Given their wide applicability, several sparse high-resolution spectral estimation techniques and their implementation have been examined in the recent literature. In this work, we further the topic by examining a computationally efficient implementation of the recent SMLA algorithms in the missing data case. The work is an extension of our implementation for the uniformly sampled case, and offers a notable computational gain as compared to the alternative implementations in the missing data case.

Place, publisher, year, edition, pages
2014. 1746-1750 p.
, European Signal Processing Conference, ISSN 2219-5491 ; 6952629
Keyword [en]
fast algorithms, Sparse Maximum Likelihood methods, Spectral estimation theory and methods, Computation theory, Signal processing, Spectrum analysis, Computational gains, Computationally efficient, High resolution, Maximum likelihood methods, Missing data, Spectral Estimation, Spectral estimation techniques, Maximum likelihood estimation
National Category
URN: urn:nbn:se:kth:diva-167596ScopusID: 2-s2.0-84911895171ISBN: 9780992862619OAI: diva2:815084
22nd European Signal Processing Conference, EUSIPCO 2014, 1 September 2014 through 5 September 2014

QC 20150529

Available from: 2015-05-29 Created: 2015-05-22 Last updated: 2015-05-29Bibliographically approved

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