Low-complexity iterative reconstruction algorithms in compressed sensing

Ludovic Danjean, Bane Vasić, Michael W. Marcellin, David Declercq

Research output: Contribution to journalConference articlepeer-review

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 otherreconstruction 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.

Original languageEnglish (US)
JournalProceedings of the International Telemetering Conference
Volume49
StatePublished - Dec 1 2013
EventITC/USA 2013: 49th Annual International Telemetering Conference and Technical Exhibition - Meeting all the Challenges of Telemetry, 2013 - Las Vegas,NV, United States
Duration: Oct 21 2013Oct 24 2013

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation
  • Computer Networks and Communications
  • Signal Processing

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