GASA Based Signal Reconstruction for Compressive Sensing
Reconstruction,which is the core of compressive sensing (CS),can be implemented by l0 norm minimization.In practice,l0 norm minimization is a NP-hard problem that requires exhaustively listing all possibilities of the original signal and is difficult to achieve by traditional algorithms.This paper proposes a signal reconstruction algorithm combining genetic algorithm with simulated annealing algorithm which is famous for their superior performance in solving combinatorial optimization problems.The method in this paper can solve l0 norm minimization directly and can reconstruct noiseless signal accurately.It has been proved through numerical simulations that the theoretical optimization performance for signal reconstruction can be achieved.The quality of reconstruction based on the proposed method is superior to that of OMP,smooth l0 norm (SL0) algorithm,Lasso and BP algorithm.
Compressive sensing l0 minimization Intelligent optimization algorithm Signal reconstruction
Dan Li Qiang Wang Yi Shen
Department of Control Science and Engineering, Harbin Institute of Technology No.92 West Da-Zhi Street, Harbin, China, 150001
国际会议
秦皇岛
英文
422-426
2015-09-18（万方平台首次上网日期，不代表论文的发表时间）