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Fusion of greedy pursuits for compressed sensing signal reconstruction
IISc - Indian Institute of Science.
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-2638-6047
IISc - Indian Institute of Science.
2012 (English)In: 2012 Proceedings Of The 20th European Signal Processing Conference (EUSIPCO), IEEE Computer Society, 2012, 1434-1438 p.Conference paper (Refereed)
Abstract [en]

Greedy Pursuits are very popular in Compressed Sensing for sparse signal recovery. Though many of the Greedy Pursuits possess elegant theoretical guarantees for performance, it is well known that their performance depends on the statistical distribution of the non-zero elements in the sparse signal. Inpractice, the distribution of the sparse signal may not be knowna priori. It is also observed that performance of Greedy Pursuits degrades as the number of available measurements decreases from a threshold value which is method dependent. To improve the performance in these situations, we introduce a novel fusion framework for Greedy Pursuits and also propose two algorithms for sparse recovery. Through Monte Carlo simulations we show that the proposed schemes improve sparse signal recovery in clean as well as noisy measurement cases.

Place, publisher, year, edition, pages
IEEE Computer Society, 2012. 1434-1438 p.
, European Signal Proceedings Conference, ISSN 2076-1465
Keyword [en]
compressed sensing, Sparse Recovery, Greedy Pursuits, Fusion
National Category
Signal Processing
Research subject
URN: urn:nbn:se:kth:diva-98493ISI: 000310623800288ScopusID: 2-s2.0-84869757839ISBN: 978-146731068-0OAI: diva2:537417
20th European Signal Processing Conference, EUSIPCO 2012;Bucharest;27 August 2012 through 31 August 2012
ICT - The Next Generation

Qc 20120827

Available from: 2012-08-27 Created: 2012-06-26 Last updated: 2013-04-15Bibliographically approved

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Chatterjee, Saikat
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Communication TheoryACCESS Linnaeus Centre
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