Non-Exemplar Incremental ISAR Target Classification via Mix-Mamba Feature Adjustment Network

To tackle the challenges of unknown image distortion and catastrophic forgetting in incremental inverse synthetic aperture radar (ISAR) target classification, this article introduces a deformation-robust non-exemplar incremental ISAR target classification method based on the Mix-Mamba feature adjustment network (MMFAN). The Mix-Mamba backbone employs channel-wise spatial transformations across multi-scale feature maps to inherently resist deformation distortions while generating compact global embedding through Mamba vision blocks. Then, the feature adjustment network facilitates knowledge transfer between base and incremental classes by dynamically maintaining a prototype for each target class. Finally, the loss bar synergizes supervised classification, unsupervised adaptation, and prototype consistency enforcement, enabling stable incremental training. Extensive experiments on ISAR datasets demonstrate the performance improvements of incremental learning and classification robustness under scaled, rotated, and mixed deformation test scenarios.