Fast ISAR Imaging of Targets Featuring Micromotion With Sparse Aperture Based on Complex Deep Unfolding Networks

The compressive sensing algorithm has been widely applied in inverse synthetic aperture radar (ISAR) imaging. However, traditional compressive sensing algorithms have limitations, such as multiple iterative steps and difficulties in selecting optimal regularization parameters. Thus, this study proposes a fast ISAR imaging method based on complex deep unfolding networks. Specifically, in the process of solving the compressive sensing algorithm, the complex sigmoid activation function is utilized to select the threshold for singular value decomposition, and the soft threshold operator is replaced with complex deep unfolding networks to enhance the denoising effect. Furthermore, a deep unfolding network based on the compressive sensing algorithm is designed. Consequently, the iterative steps of compressive sensing algorithm are effectively reduced, and a large number of pre-experiments required for selecting the regularization parameters are avoided, thereby improving computational efficiency and the adaptability. The proposed algorithm can rapidly and accurately generate ISAR images of targets featuring micro-motion with sparse aperture, and achieves satisfactory results in both simulated and measured data.