DocumentCode :
2080855
Title :
Multi-sparse signal recovery for compressive sensing
Author :
Yipeng Liu ; Gligorijevic, I. ; Matic, Vladimir ; De Vos, Maarten ; Van Huffel, Sabine
Author_Institution :
Dept. of Electr. Eng., KU Leuven, Leuven, Belgium
fYear :
2012
fDate :
Aug. 28 2012-Sept. 1 2012
Firstpage :
1053
Lastpage :
1056
Abstract :
Signal recovery is one of the key techniques of compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one domain to formulate the L0 norm optimization. Recent investigation shows that some signals are sparse in multiple domains. To further improve the signal reconstruction performance, we can exploit this multi-sparsity to generate a new convex programming model. The latter is formulated with multiple sparsity constraints in multiple domains and the linear measurement fitting constraint. It improves signal recovery performance by additional a priori information. Since some EMG signals exhibit sparsity both in time and frequency domains, we take them as example in numerical experiments. Results show that the newly proposed method achieves better performance for multi-sparse signals.
Keywords :
compressed sensing; convex programming; electromyography; medical signal processing; signal reconstruction; time-frequency analysis; EMG signals; L0 norm optimization; a priori information; classical methods; compressive sensing; convex programming model; frequency domains; linear measurement fitting constraint; linear subNyquist measurements; multiple domains; multisparse signal recovery; original signal reconstruction; time domains; Electromyography; Frequency domain analysis; Matching pursuit algorithms; Optimization; Programming; Sparse matrices; Vectors; Animals; Electromyography; Humans; Models, Biological; Signal Processing, Computer-Assisted;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
Conference_Location :
San Diego, CA
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4119-8
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/EMBC.2012.6346115
Filename :
6346115
Link To Document :
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