Abstract / truncated to 115 words (read the full abstract)

Compressed sensing (CS) is a recently introduced signal acquisition framework that goes against the traditional Nyquist sampling paradigm. CS demonstrates that a sparse, or compressible, signal can be acquired using a low rate acquisition process. Since noise is always present in practical data acquisition systems, sensing and reconstruction methods are developed assuming a Gaussian (light-tailed) model for the corrupting noise. However, when the underlying signal and/or the measurements are corrupted by impulsive noise, commonly employed linear sampling operators, coupled with Gaussian-derived reconstruction algorithms, fail to recover a close approximation of the signal. This dissertation develops robust sampling and reconstruction methods for sparse signals in the presence of impulsive noise. To achieve this objective, we make ... toggle 5 keywords

compressed sensing sampling methods signal reconstruction nonlinear estimation impulse noise

Information

Author
Carrillo, Rafael
Institution
University of Delaware
Supervisor
Publication Year
2012
Upload Date
Dec. 18, 2013

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