A Bat-Inspired Sparse Recovery Algorithm for Compressed Sensing. (29th October 2018)

• Record Type: Journal Article
• Authors: Bao, Wanning; Liu, Haiqiang; Huang, Dongbo; Hua, Qianqian; Hua, Gang
• Other Names: Del Giudice Paolo Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2018(2018)
• Publication Date: 2018-10-29
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2018/1365747
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Compressed sensing (CS) is an important research area of signal sampling and compression, and the essence of signal recovery in CS is an optimization problem of solving the underdetermined system of equations. Greedy pursuit algorithms are widely used to solve this problem. They have low computational complexity; however, their recovery performance is limited. In this paper, an intelligence recovery algorithm is proposed by combining the Bat Algorithm (BA) and the pruning technique in subspace pursuit. Experimental results illustrate that the proposed algorithm has better recovery performance than greedy pursuit algorithms. Moreover, applied to the microseismic monitoring system, the BA can recover the signal well.