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 language | English (US) |
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| Journal | Proceedings of the International Telemetering Conference |
| Volume | 49 |
| State | Published - 2013 |
| Event | ITC/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 2013 → Oct 24 2013 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Instrumentation
- Computer Networks and Communications
- Signal Processing