A Novel Downward-Looking Linear Array SAR Imaging Method Based on Multiple Measurement Vector Model with L2,1-Norm

• Published in: Journal of sensors Vol. 2021
• Main Authors: Kang, Le, Sun, Tian-chi, Ni, Jia-cheng, Zhang, Qun, Luo, Ying
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; Hindawi Limited
• Subjects: Algorithms; Antennas; Domains; Linear arrays; Matched pursuit; Noise; Radar arrays; Radar imaging; Regularization; Remote sensing; Signal processing; Signal to noise ratio; Synthetic aperture radar
• ISSN: 1687-725X; 1687-7268
• DOI: 10.1155/2021/3775222

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 L2,1-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 L2,1-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 L2,1-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.