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One-Bit Compressed Sensing by Greedy Algorithms

Published online by Cambridge University Press:  24 May 2016

Wenhui Liu ,
Da Gong  and
Zhiqiang Xu
Wenhui Liu*
Affiliation:
LSEC, Institute of Computational Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Da Gong*
Affiliation:
LSEC, Institute of Computational Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Zhiqiang Xu*
Affiliation:
LSEC, Institute of Computational Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
*
*Corresponding author. Email addresses: [email protected] (W. -H. Liu), [email protected] (D. Gong), [email protected] (Z. -Q. Xu)
*Corresponding author. Email addresses: [email protected] (W. -H. Liu), [email protected] (D. Gong), [email protected] (Z. -Q. Xu)
*Corresponding author. Email addresses: [email protected] (W. -H. Liu), [email protected] (D. Gong), [email protected] (Z. -Q. Xu)
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Abstract

Sign truncated matching pursuit (STrMP) algorithm is presented in this paper. STrMP is a new greedy algorithm for the recovery of sparse signals from the sign measurement, which combines the principle of consistent reconstruction with orthogonal matching pursuit (OMP). The main part of STrMP is as concise as OMP and hence STrMP is simple to implement. In contrast to previous greedy algorithms for one-bit compressed sensing, STrMP only need to solve a convex and unconstrained subproblem at each iteration. Numerical experiments show that STrMP is fast and accurate for one-bit compressed sensing compared with other algorithms.

Keywords

94A1265H9965D15Compressed sensingsparse signalsgreedy algorithm
Type
Research Article
Information
Numerical Mathematics: Theory, Methods and Applications , Volume 9 , Issue 2 , May 2016 , pp. 169 - 184
Copyright
Copyright © Global-Science Press 2016 

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