KAN-KT-Unet: Keystone Transform-Based Method for ISAR Range Migration Compensation and Auto-Focusing

Clear inverse synthetic aperture radar (ISAR) images are essential for applications such as area surveillance and target recognition. Achieving high-resolution ISAR imaging often requires wide signal bandwidth and significant target rotation during the imaging period. However, large rotation angles usually induce range migration, complicating the imaging process. While existing methods typically employ the keystone transform to correct range migration, they may fail under complex maneuvering conditions. Additionally, these methods usually neglect azimuth defocusing, which significantly impacts ISAR imaging clarity. This study addresses these challenges by proposing a KAN-KT-Unet model. This model can compensate for range migration using parametric resampling and keystone transform, which accounts for first-order and second-order rotational components. An adaptive parameter extraction network based on the Kolmogorov-Arnold network (KAN) replaces traditional iterative search processes, improving efficiency and accuracy. Additionally, azimuth defocusing is mitigated through an enhanced Unet architecture with reduced channels, balancing resolution improvement and noise suppression. Simulation and measured-data experiments validate the effectiveness of the proposed method, demonstrating its superior generalization and noise resistance compared to traditional algorithms. These results highlight the KAN-KT-Unet model's potential for improving ISAR imaging quality under maneuvering-target and noisy conditions, making it suitable for practical applications.