Continuous Compressed Sensing With a Single or Multiple Measurement Vectors
arXiv:1405.6544 · doi:10.1109/SSP.2014.6884632
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 -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.
4 pages, 2 figures, in IEEE Workshop on Statistical Signal Processing (SSP), pp. 308--311, June 2014