Continuous compressed sensing with a single or multiple measurement vectors

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Authors Z. Yang and L. Xie
Journal/Conference Name EEE Workshop on Statistical Signal Processing (SSP)
Paper Category
Paper Abstract We consider the problem of recovering a single or multiple frequency-sparse signals, which share the same frequency components, from a subset of regularly spaced samples. The problem is referred to as continuous compressed sensing (CCS) in which the frequencies can take any values in the normalized domain [0,1). In this paper, a link between CCS and low rank matrix completion (LRMC) is established based on an ℓ0-pseudo-norm-like formulation, and theoretical guarantees for exact recovery are analyzed. Practically efficient algorithms are proposed based on the link and convex and nonconvex relaxations, and validated via numerical simulations.
Date of publication 2014
Code Programming Language MATLAB

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