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Weighted covariance matching based square root LASSO
2015 (English)In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, IEEE conference proceedings, 2015, 3751-3755 p.Conference paper, Published paper (Refereed)
Resource type
Text
Abstract [en]

We propose a method for high dimensional sparse estimation in the multiple measurement vector case. The method is based on the covariance matching technique and with a sparse penalty along the ideas of the square-root LASSO (sr-LASSO). The method not only benefits from the strong characteristics of sr-LASSO (independence of the hyper-parameter selection from the noise variance), but also offers a performance near maximum likelihood. It performs close to the Cramer-Rao bound even at low signal to noise ratios and it is generalized to manage correlated noise. The only assumption in this matter is that the noise covariance matrix structure is known. The numerical simulation provided in an array processing application illustrates the potential of the method.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. 3751-3755 p.
Keyword [en]
correlated noise, covariance matching, multiple measurements, sparse estimation
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-181511DOI: 10.1109/ICASSP.2015.7178672Scopus ID: 2-s2.0-84946042307ISBN: 9781467369978 (print)OAI: oai:DiVA.org:kth-181511DiVA: diva2:912329
Conference
40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015, 19 April 2014 through 24 April 2014
Note

QC 20160316

Available from: 2016-03-16 Created: 2016-02-02 Last updated: 2016-04-25Bibliographically approved

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  • apa
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Language
  • de-DE
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  • Other locale
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Output format
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  • text
  • asciidoc
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