DocumentCode :
186566
Title :
Artificial immune algorithm based signal reconstruction for compressive sensing
Author :
Dan Li ; Chunli Shi ; Qiang Wang ; Yi Shen ; Yan Wang
Author_Institution :
Dept. of Control Sci. & Eng., Harbin Inst. of Technol., Harbin, China
fYear :
2014
fDate :
12-15 May 2014
Firstpage :
76
Lastpage :
81
Abstract :
The core of compressive sensing, i.e., signal reconstruction, is a constraint of signal sparsity problem, which can be implemented by l0 norm minimization. But l0 norm minimization requires exhaustively listing all possibility of the original signals, which is an NP-hard problem to achieve difficultly by traditional algorithm.This paper proposes a signal reconstruction algorithm based on artificial immune algorithm, which can solve l0 norm minimization directly. It has been proved through numerical simulations that performance of signal reconstruction and photo-acoustic image reconstruction based on the proposed method is superior to that of OMP algorithm.
Keywords :
artificial immune systems; compressed sensing; numerical analysis; signal reconstruction; NP-hard problem; artificial immune algorithm; compressive sensing; norm minimization; numerical simulations; photo acoustic image reconstruction; signal reconstruction; signal reconstruction algorithm; signal sparsity problem; Compressed sensing; Image reconstruction; Immune system; Matching pursuit algorithms; Minimization; Signal processing algorithms; Signal reconstruction; Artificial Immune Algorithm; Compressive sensing; Signal reconstruction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International
Conference_Location :
Montevideo
Type :
conf
DOI :
10.1109/I2MTC.2014.6860526
Filename :
6860526
Link To Document :
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