Low-Complexity Iterative Reconstruction Algorithms in Compressed Sensing
Author
Danjean, LudovicAdvisor
Vasić, BaneMarcellin, Michael W.
Declercq, David
Affiliation
University of ArizonaIssue Date
2013-10
Metadata
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Copyright © held by the author; distribution rights International Foundation for TelemeteringCollection Information
Proceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.Abstract
In this paper we focus on two low-complexity iterative reconstruction algorithms in compressed sensing. These algorithms, called the approximate message-passing algorithm and the interval-passing algorithm, are suitable to recover sparse signals from a small set of measurements. Depending on the type of measurement matrix (sparse or random) used to acquire the samples of the signal, one or the other reconstruction algorithm can be used. We present the reconstruction results of these two reconstruction algorithms in terms of proportion of correct reconstructions in the noise free case. We also report in this paper possible practical applications of compressed sensing where the choice of the measurement matrix and the reconstruction algorithm are often governed by the constraint of the considered application.Sponsors
International Foundation for TelemeteringISSN
0884-51230074-9079