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Fusion of algorithms for Compressed Sensing
KTH, School of Electrical Engineering (EES), Communication Theory.ORCID iD: 0000-0003-2638-6047
2013 (English)In: ICASSP IEEE Int Conf Acoust Speech Signal Process Proc, 2013, 5860-5864 p.Conference paper (Refereed)
Abstract [en]

Numerous algorithms have been proposed recently for sparse signal recovery in Compressed Sensing (CS). In practice, the number of measurements can be very limited due to the nature of the problem and/or the underlying statistical distribution of the non-zero elements of the sparse signal may not be known a priori. It has been observed that the performance of any sparse signal recovery algorithm depends on these factors, which makes the selection of a suitable sparse recovery algorithm difficult. To take advantage in such situations, we propose to use a fusion framework using which we employ multiple sparse signal recovery algorithms and fuse their estimates to get a better estimate. Theoretical results justifying the performance improvement are shown. The efficacy of the proposed scheme is demonstrated by Monte Carlo simulations using synthetic sparse signals and ECG signals selected from MIT-BIH database.

Place, publisher, year, edition, pages
2013. 5860-5864 p.
, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, ISSN 1520-6149
Keyword [en]
Compressed Sensing, Fusion, Signal Reconstruction, Sparse Recovery, Support Recovery
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-140041DOI: 10.1109/ICASSP.2013.6638788ScopusID: 2-s2.0-84888097847ISBN: 9781479903566OAI: diva2:689553
2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, 26 May 2013 through 31 May 2013, Vancouver, BC

QC 20140121

Available from: 2014-01-21 Created: 2014-01-16 Last updated: 2014-01-21Bibliographically approved

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Chatterjee, Saikat
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Communication Theory
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