A Novel Deep Unfolding Network for Multi-Band SAR Sparse Imaging and Autofocusing

The sparse imaging network of synthetic aperture radar (SAR) is usually designed end to end and has a limited adaptability to radar systems of different bands. Meanwhile, the implementation of the sparse imaging algorithm depends on the sparsity of the target scene and usually adopts a fixed L1 regularization solution, which has a mediocre reconstruction effect on complex scenes. In this paper, a novel SAR imaging deep unfolding network based on approximate observation is proposed for multi-band SAR systems. Firstly, the approximate observation module is separated from the optimal solution network model and selected according to the multi-band radar echo. Secondly, to realize the SAR imaging of non-sparse scenes, Lp regularization is used to constrain the uncertain transform domain of the target scene. The adaptive optimization of Lp parameters is realized by using a data-driven approach. Furthermore, considering that phase errors may be introduced in the real SAR system during echo acquisition, an error estimation module is added to the network to estimate and compensate for the phase errors. Finally, the results from both simulated and real data experiments demonstrate that the proposed method exhibits outstanding performance under 0.22 THz and 9.6 GHz echo data: high-resolution SAR focused images are achieved under four different sparsity conditions of 20%, 40%, 60%, and 80%. These results fully validate the strong adaptability and robustness of the proposed method to diverse SAR system configurations and complex target scenarios.