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Beamformers For Sparse Recovery
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6855-5868
2013 (English)In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New York: IEEE , 2013, 5920-5924 p.Conference paper, Published paper (Refereed)
Abstract [en]

In sparse recovery from measurement data a common approach is to use greedy pursuit reconstruction algorithms. Most of these algorithms have a correlation filter for detecting active components in the sparse data. In this paper, we show how modifications can be made for the greedy pursuit algorithms so that they use beamformers insteadof the standard correlation filter. Using these beamformers, improved performance in the algorithms is obtained. In particular, we discuss beamformers for the average and worst case scenario and give methods for constructing them.

Place, publisher, year, edition, pages
New York: IEEE , 2013. 5920-5924 p.
Series
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, ISSN 1520-6149
Keyword [en]
Compressed sensing, Greedy pursuit algorithms, Beamforming
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-123993DOI: 10.1109/ICASSP.2013.6638800ISI: 000329611506015Scopus ID: 2-s2.0-84890494911ISBN: 978-1-4799-0356-6 (print)OAI: oai:DiVA.org:kth-123993DiVA: diva2:651909
Conference
2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013; Vancouver, BC; Canada; 26 May 2013 through 31 May 2013
Note

QC 20131022

Available from: 2013-09-27 Created: 2013-06-24 Last updated: 2014-02-25Bibliographically approved

Open Access in DiVA

fulltext(302 kB)