A Swarm Intelligence Algorithm for Joint Sparse Recovery

作者：Du, Xinpeng^*; Cheng, Lizhi; Liu, Lufeng

来源：IEEE Signal Processing Letters, 2013, 20(6): 611-614.

DOI：10.1109/LSP.2013.2260822

• Compressed sensing
• multiple measurement vector
• particle swarm optimization
• thresholding

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.

• 出版日期2013-6
• 单位中国人民解放军国防科学技术大学