TY - GEN
T1 - GA-BFO based signal reconstruction for compressive sensing
AU - Li, Dan
AU - Li, Muyu
AU - Shen, Yi
AU - Wang, Yan
AU - Wang, Qiang
PY - 2013
Y1 - 2013
N2 - The theory of compressive sensing (CS) mainly includes three aspects, i.e., sparse representation, uncorrelated sampling, and signal reconstruction, in which signal reconstruction serve as the core of CS. The constraint of signal sparsity can be implemented by l0 norm minimization, which is an NP-hard problem that requires exhaustively listing all possibilities of the original signal and is difficult to achieve by the traditional algorithm. This paper proposes a signal reconstruction algorithm based on intelligent optimization algorithm which combines genetic algorithm (GA) and Bacteria Foraging Optimization (BFO) algorithm. This method can find the global optimal solution by genetic and evolutionary operation to the group, which can solve l0 norm minimization directly. It has been proved through numerical simulations that the theoretical optimization performance can be achieved and the result is superior to that of OMP algorithm.
AB - The theory of compressive sensing (CS) mainly includes three aspects, i.e., sparse representation, uncorrelated sampling, and signal reconstruction, in which signal reconstruction serve as the core of CS. The constraint of signal sparsity can be implemented by l0 norm minimization, which is an NP-hard problem that requires exhaustively listing all possibilities of the original signal and is difficult to achieve by the traditional algorithm. This paper proposes a signal reconstruction algorithm based on intelligent optimization algorithm which combines genetic algorithm (GA) and Bacteria Foraging Optimization (BFO) algorithm. This method can find the global optimal solution by genetic and evolutionary operation to the group, which can solve l0 norm minimization directly. It has been proved through numerical simulations that the theoretical optimization performance can be achieved and the result is superior to that of OMP algorithm.
KW - Bacteria foraging optimization
KW - Compressive sensing
KW - Genetic algorithm
KW - Signal reconstruction
UR - https://www.scopus.com/pages/publications/84894200674
U2 - 10.1109/ICInfA.2013.6720445
DO - 10.1109/ICInfA.2013.6720445
M3 - 会议稿件
AN - SCOPUS:84894200674
SN - 9781479913343
T3 - 2013 IEEE International Conference on Information and Automation, ICIA 2013
SP - 1023
EP - 1028
BT - 2013 IEEE International Conference on Information and Automation, ICIA 2013
T2 - 2013 IEEE International Conference on Information and Automation, ICIA 2013
Y2 - 26 August 2013 through 28 August 2013
ER -