CONTINUOUS COMPRESSED SENSING WITH A SINGLE OR MULTIPLE MEASUREMENT VECTORS

被引：0
|
作者：
Yang, Zai [1 ]
Xie, Lihua [1 ]
机构：
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
来源：
2014 IEEE WORKSHOP ON STATISTICAL SIGNAL PROCESSING (SSP) | 2014年
关键词：
Continuous compressed sensing; multiple measurement vectors (MMV); atomic norm; DOA estimation;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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 l(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.
引用
收藏
页码：288 / 291
页数：4
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