Continual NeRF-Based 3D ISAR Imaging With Multilevel Distillation

In 3D inverse synthetic aperture radar (ISAR) imaging of space targets, radar neural radiance fields (i.e., ISAR-NeRF) is an important research direction. However, a significant yet unexplored problem is its deployment in continual learning scenarios, where gradual 3D imaging is expected since target ISAR images often emerge sequentially. To address this issue, this letter proposes a new continual 3D ISAR imaging method, named CL-ISAR-NeRF. Specifically, CL-ISAR-NeRF leverages a multilevel distillation mechanism to simultaneously replay pixel, field, and feature-levels information, to alleviate the forgetting of previously learned knowledge. In addition, an efficient memory selection strategy is designed to enrich the diversity of line-of-sight (LOS) when selecting replayed data, which improves imaging performance and further enhances the stability and plasticity of the method. In order to evaluate the proposed method in continual learning settings, we design a realistic simulation scenario in which the trajectories of space targets are calculated by the Simplified General Perturbations-4 (SGP4) model. The comparative experiments with classic continual learning methods demonstrate the superior performance and robustness of CL-ISAR-NeRF.