A Metalearning-based Sparse Aperture ISAR Imaging Method

Sparse Aperture-Inverse Synthetic Aperture Radar (SA-ISAR) imaging methods aim to reconstruct high-quality ISAR images from the corresponding incomplete ISAR echoes. The existing SA-ISAR imaging methods can be roughly divided into two categories: model-based and deep learning-based methods. Model-based SA-ISAR methods comprise physical ISAR imaging models based on explicit mathematical formulations. However, due to the high nonconvexity and ill-posedness of the SA-ISAR problem, model-based methods are often ineffective compared with deep learning-based methods. Meanwhile, the performance of the existing deep learning-based methods depends on the quality and quantity of the training data, which are neither sufficient nor precisely labeled in space target SA-ISAR imaging tasks. To address these issues, we propose a metalearning-based SA-ISAR imaging method for space target ISAR imaging tasks. The proposed method comprises two primary modules: the learning-aided alternating minimization module and the metalearning-based optimization module. The learning-aided alternating minimization module retains the explicit ISAR imaging formulations, guaranteeing physical interpretability without data dependency. The metalearning-based optimization module incorporates a non-greedy strategy to enhance convergence performance, ensuring the ability to escape from poor local modes during optimization. Extensive experiments validate that the proposed algorithm demonstrates superior performance, excellent generalization capability, and high efficiency, despite the lack of prior training or access to labeled training samples, compared to existing methods.