An Overview Of Robust Compressive Sensing Of Sparse Signals In Impulsive Noise
2015 23rd European Signal Processing Conference (EUSIPCO)(2015)
摘要
While compressive sensing (CS) has traditionally relied on l(2) as an error norm, a broad spectrum of applications has emerged where robust estimators are required. Among those, applications where the sampling process is performed in the presence of impulsive noise, or where the sampling of the high-dimensional sparse signals requires the preservation of a distance different than l(2). This article overviews robust sampling and nonlinear reconstruction strategies for sparse signals based on the Cauchy distribution and the Lorentzian norm for the data fidelity. The derived methods outperform existing compressed sensing techniques in impulsive environments, thus offering a robust framework for CS.
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关键词
Compressed sensing,sampling methods,robust signal reconstruction,nonlinear estimation,impulsive noise
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