DocumentCode :
1783787
Title :
Robust iterative hard thresholding for compressed sensing
Author :
Ollila, Esa ; Hyon-Jung Kim ; Koivunen, Visa
Author_Institution :
Dept. of Signal Process. & Acoust., Aalto Univ., Aalto, Finland
fYear :
2014
fDate :
21-23 May 2014
Firstpage :
226
Lastpage :
229
Abstract :
Compressed sensing (CS) or sparse signal reconstruction (SSR) is a signal processing technique that exploits the fact that acquired data can have a sparse representation in some basis. One popular technique to reconstruct or approximate the unknown sparse signal is the iterative hard thresholding (IHT) which however performs very poorly under non-Gaussian noise conditions or in the face of outliers (gross errors). In this paper, we propose a robust IHT method based on ideas from M-estimation that estimates the sparse signal and the scale of the error distribution simultaneously. The method has a negligible performance loss compared to IHT under Gaussian noise, but superior performance under heavy-tailed non-Gaussian noise conditions.
Keywords :
Gaussian noise; compressed sensing; iterative methods; signal reconstruction; CS; Gaussian noise; IHT; SSR; compressed sensing; error distribution; iterative hard thresholding; nonGaussian noise conditions; robust IHT method; robust iterative hard thresholding; signal processing technique; sparse representation; sparse signal; sparse signal reconstruction; Approximation methods; Compressed sensing; Robustness; Signal to noise ratio; Vectors; Compressed sensing; M-estimation; iterative hard thresholding; robust estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications, Control and Signal Processing (ISCCSP), 2014 6th International Symposium on
Conference_Location :
Athens
Type :
conf
DOI :
10.1109/ISCCSP.2014.6877856
Filename :
6877856
Link To Document :
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