TY - GEN
T1 - Continuous compressed sensing with a single or multiple measurement vectors
AU - Yang, Zai
AU - Xie, Lihua
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Continuous compressed sensing
KW - DOA estimation
KW - atomic norm
KW - multiple measurement vectors (MMV)
UR - https://www.scopus.com/pages/publications/84907413769
U2 - 10.1109/SSP.2014.6884632
DO - 10.1109/SSP.2014.6884632
M3 - 会议稿件
AN - SCOPUS:84907413769
SN - 9781479949755
T3 - IEEE Workshop on Statistical Signal Processing Proceedings
SP - 288
EP - 291
BT - 2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
PB - IEEE Computer Society
T2 - 2014 IEEE Workshop on Statistical Signal Processing, SSP 2014
Y2 - 29 June 2014 through 2 July 2014
ER -