scholarly journals On the Fundamental Limit of Orthogonal Matching Pursuit for Multiple Measurement Vector

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 48860-48866 ◽  
Author(s):  
Haifeng Li ◽  
Libo Wang ◽  
Xueqiu Zhan ◽  
Deepak Kumar Jain
Keyword(s):  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Multiple Measurement ◽  
Fundamental Limit ◽  
Multiple Measurement Vector ◽  
Measurement Vector

scholarly journals A Novel Downward-Looking Linear Array SAR Imaging Method Based on Multiple Measurement Vector Model with L 2 , 1 -Norm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Le Kang ◽  
Tian-chi Sun ◽  
Jia-cheng Ni ◽  
Qun Zhang ◽  
Ying Luo
Keyword(s):  
Linear Array ◽  
Imaging System ◽  
Matching Pursuit ◽  
Three Dimensional ◽  
Vector Model ◽  
Imaging Method ◽  
Sparse Regularization ◽  
Multiple Measurement ◽  
Multiple Measurement Vector ◽  
Measurement Vector

Downward-looking linear array synthetic aperture radar (DLLA SAR) is a kind of three-dimensional (3-D) radar imaging system. To obtain the superresolution along the crosstrack direction of DLLA SAR, the sparse regularization models with single measurement vector (SMV) have been widely applied. However, the robustness of the sparse regularization models with SMV is unsatisfactory, especially in the low signal-to-noise rate (SNR) environment. To solve this problem, we proposed a novel imaging method for DLLA SAR based on the multiple measurement vector (MMV) model with L 2 , 1 -norm. At first, we exchange the processing order between the along-track (AT) domain and the crosstrack (CT) domain to keep the same sparse structure of the signal in the crosstrack domain so that we can establish the imaging problem as a sparse regularization model based on the MMV model. Moreover, the mixed L 2 , 1 -norm is introduced into the regularization term of the MMV model. Finally, the modified orthogonal matching pursuit (OMP) algorithm is designed for the MMV model with the L 2 , 1 -norm. The simulations verify that the proposed method has better performance in the lower SNR environment and requires lower computation compared with the conventional methods.

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