DocumentCode :
40024
Title :
Robust Sparse Blind Source Separation
Author :
Chenot, Cecile ; Bobin, Jerome ; Rapin, Jeremy
Author_Institution :
Service d´Astrophys., SEDI, CEA, Gif-sur-Yvette, France
Volume :
22
Issue :
11
fYear :
2015
fDate :
Nov. 2015
Firstpage :
2172
Lastpage :
2176
Abstract :
Blind source separation is a widely used technique to analyze multichannel data. In many real-world applications, its results can be significantly hampered by the presence of unknown outliers. In this paper, a novel algorithm coined rGMCA (robust Generalized Morphological Component Analysis) is introduced to retrieve sparse sources in the presence of outliers. It explicitly estimates the sources, the mixing matrix, and the outliers. It also takes advantage of the estimation of the outliers to further implement a weighting scheme, which provides a highly robust separation procedure. Numerical experiments demonstrate the efficiency of rGMCA to estimate the mixing matrix in comparison with standard BSS techniques.
Keywords :
blind source separation; matrix algebra; mixing matrix; multichannel data analysis; rGMCA; robust generalized morphological component analysis; robust sparse blind source separation; sparse source retrieval; Blind source separation; Estimation; Gaussian noise; Robustness; Signal processing algorithms; Standards; Blind source separation; outliers; robust recovery; sparse representations; sparsity;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2015.2463232
Filename :
7194778
Link To Document :
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