Title :
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
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;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2013.2260822
