Continuous Compressed Sensing With a Single or Multiple Measurement Vectors

Zai Yang; Lihua Xie

doi:10.1109/ssp.2014.6884632

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arXiv · 1405.6544

Continuous Compressed Sensing With a Single or Multiple Measurement Vectors

Zai Yang·Lihua Xie

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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 $\ell_0$-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.

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Zai Yang, Lihua Xie. 2014-05-26. Continuous Compressed Sensing With a Single or Multiple Measurement Vectors. https://doi.org/10.1109/ssp.2014.6884632

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