A Swarm Intelligence Algorithm for Joint Sparse Recovery

Author

Xinpeng Du ; Lizhi Cheng ; Lufeng Liu

Author_Institution

Dept. of Math. & Syst. Sci., Nat. Univ. of Defense Technol., Changsha, China

Volume

20

Issue

6

fYear

2013

fDate

Jun-13

Firstpage

611

Lastpage

614

Abstract

This letter addresses the joint sparse recovery problem, which is a hot topic in the compressed sensing (CS) theory and its various applications. Inspired by particle swarm optimization (PSO) algorithm and some sparse recovery algorithms, a novel swarm intelligence algorithm called M-SISR is proposed to solve the problem. In M-SISR, the initial positions of the swarm are designed using the q-thresholding (1 ≤ q ≤ 2) algorithm, and the update strategy is designed using the ideas of PSO and some sparse recovery algorithms. Theoretical analysis shows the good property of the update strategy, and numerical simulations on random Gaussian data illustrate the efficiency of M-SISR.

Keywords

Gaussian processes; compressed sensing; particle swarm optimisation; M-SISR; compressed sensing theory; joint sparse recovery problem; particle swarm optimization algorithm; q-thresholding (1 ≤ q ≤ 2) algorithm; random Gaussian data; swarm intelligence algorithm; Algorithm design and analysis; Joints; Matching pursuit algorithms; Particle swarm optimization; Signal processing algorithms; Sparse matrices; Vectors; Compressed sensing; multiple measurement vector; particle swarm optimization; thresholding;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2013.2260822

Filename

6510538