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Efficient spectral ...
Efficient spectral analysis in the missing data case using sparse ML methods
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Glentis, G. -O (author)
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- Karlsson, Johan (author)
- KTH,Optimeringslära och systemteori
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- Jakobsson, Andreas (author)
- Lund University,Lunds universitet,Biomedical Modelling and Computation,Forskargrupper vid Lunds universitet,Statistical Signal Processing Group,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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Li, J. (author)
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(creator_code:org_t)
- 2014
- 2014
- English.
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Series: European Signal Processing Conference, 2219-5491 ; 6952629
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In: European Signal Processing Conference. - 2219-5491.
- Related links:
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http://www.eurasip.o... (free)
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https://urn.kb.se/re...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Matematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics (hsv//eng)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Keyword
- 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
- Spectral estimation theory and methods
- Sparse Maximum Likelihood methods
- fast algorithms
Publication and Content Type
- ref (subject category)
- kon (subject category)
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