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Name: John Nolan

Talk Title: Robust Sparse Reconstruction

Talk Abstract: Traditional compressive sensing (CS) assumes light-tailed models for the underlying signal and/or noise. This assumption is not met in the case of highly impulsive environments, where non-Gaussian processes arise. In this case, traditional sparse reconstruction methods perform poorly, since they are incapable of suppressing the effects of heavy-tailed sampling noise. This talk will describe the use of heavy-tailed stable distributions to design a robust algorithm for sparse signal reconstruction from linear random measurements corrupted by infinite-variance additive noise. We demonstrate the improved reconstruction performance of the proposed algorithm when compared against standard CS techniques for a broad range of impulsive environments.

This talk is an invited talk at EVA 2021.