Learning-Based Sparse Recovery Algorithm for 3D SAR Imaging

The compressed sensing (CS) method is widely utilized in the field of radar sparse imaging. However, it always encounters enormous iterations and low generalizability. To solve these problems, in this paper, we propose a novel learning-based sparse imaging network architecture, i.e., Split Unfolding Sparsity-Driven Network (SSD-Net), for 3D synthetic aperture radar (SAR) imaging. By combining the model-based SAR imaging method and data-driven deep learning method, SSD-Net has favorable explainability and generalization abil-ity to produce 3D SAR images. The deep hierarchical ar-chitecture of SSD-Net is obtained by combiningthe radar nonlinear operator and the split Bregman method. The exper-iments demonstrate that the proposed SSD-Net outperforms other state-of-the-art methods in the field of SAR imaging.