Continuous compressed sensing with a single or multiple measurement vectors
By: Researcher II
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| Authors | Z. Yang and L. Xie |
| Journal/Conference Name | EEE Workshop on Statistical Signal Processing (SSP) |
| Paper Category | ECE |
| 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 |
| Comment |
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