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Fusion of algorithms for Compressed Sensing
KTH, Skolan för elektro- och systemteknik (EES), Kommunikationsteori.ORCID-id: 0000-0003-2638-6047
2013 (engelsk)Inngår i: ICASSP IEEE Int Conf Acoust Speech Signal Process Proc, 2013, s. 5860-5864Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2013. s. 5860-5864
Serie
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, ISSN 1520-6149
Emneord [en]
Compressed Sensing, Fusion, Signal Reconstruction, Sparse Recovery, Support Recovery
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-140041DOI: 10.1109/ICASSP.2013.6638788Scopus ID: 2-s2.0-84888097847ISBN: 9781479903566 (tryckt)OAI: oai:DiVA.org:kth-140041DiVA, id: diva2:689553
Konferanse
2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, 26 May 2013 through 31 May 2013, Vancouver, BC
Merknad

QC 20140121

Tilgjengelig fra: 2014-01-21 Laget: 2014-01-16 Sist oppdatert: 2014-01-21bibliografisk kontrollert

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Totalt: 317 treff
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