Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings

被引:0
|
作者
Masoumi, Hamed [1 ]
Verhaegen, Michel [1 ]
Myers, Nitin Jonathan [1 ]
机构
[1] Delft Univ Technol, Delft Ctr Syst & Control, Delft, Netherlands
来源
2023 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP, SSP | 2023年
关键词
Compressive sensing; orthogonal matching pursuit; support recovery; mutual coherence; SPARSE SIGNAL RECOVERY; SUPPORT RECOVERY; GUARANTEES;
D O I
10.1109/SSP53291.2023.10207984
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for sparse signal recovery in compressed sensing (CS). Prior work on OMP, however, has only provided reconstruction guarantees under the assumption that the columns of the CS matrix have equal norms, which is unrealistic in many practical CS applications due to hardware constraints. In this paper, we derive sparse recovery guarantees with OMP, when the CS matrix has unequal column norms. Finally, we show that CS matrices whose column norms are comparable achieve tight guarantees for the successful recovery of the support of a sparse signal and a low mean squared error in the estimate.
引用
收藏
页码:170 / 174
页数:5
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