1 research outputs found
Robust iterative hard thresholding for compressed sensing
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 -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.Comment: To appear in Proc. of ISCCSP 201