Tuning-Free ISAR Imaging Based on Single-Step Deep Reinforcement Learning With Swin Transformer

作者：Xiaoyong Li, Jiale Huang, Xueru Bai, Lei Liu, Xiaoran Shi, Feng Zhou · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2025 · DOI：10.1109/tgrs.2025.3534191 · 被引用次数：9 · 研究领域：Advanced Optical Sensing Technologies、Spectroscopy Techniques in Biomedical and Chemical Research、Photoacoustic and Ultrasonic Imaging

Because of the constraints of observation conditions, it is difficult to obtain a large amount of measured data for real targets in the inverse synthetic aperture radar (ISAR) system. Existing deep networks usually use the simulated data of random points for training, which will lead to the degradation of the imaging performance of measured data when the distribution of measured data is different from that of simulated data, i.e., poor generalization performance. A high-resolution ISAR imaging method based on Swin Transformer-based deep reinforcement learning (SwinRL) is proposed to address this problem. The 2-D alternating direction method of multipliers (ADMM) is modeled as a sequential decision problem in this method. The internal adjustable parameters are modeled as actions, and the Swin Transformer is used as the backbone network of the policy network and value network. The optimization of the actions, i.e., the internal adjustable parameters of the 2-D ADMM algorithm, is then guided through network training in a reinforcement learning framework. After that, the trained agent can automatically give optimal internal parameters according to different input data, and then well-focused imaging results can be obtained by executing a 2-D ADMM algorithm with optimal parameters. Finally, experimental results based on simulated and measured data show the performance priority of the proposed method compared to existing deep unrolling networks with fixed parameters.