Inverse Synthetic Aperture Radar Imaging Using Group Based Dictionary Learning

Abstract

As compared with traditional radar imaging methods, the compressive sensing (CS) radar imaging methods can obtain high-quality images using much less data. However, the default assumption that the target scene is sparse actually limits the performance of the CS based ISAR imaging methods. The dictionary learning (DL) has been used to find the sparse representation of the target scene where a block processing is used, but each image patch is considered independently and the relationship between the patches is not utilized. In this paper, we propose an improved DL method for ISAR imaging exploiting the idea of group sparsity. First, we use image patches with similar structure to construct several groups. Then, we utilize the SVD technique to learn a sparse transform inferred from the image patch groups. This learnt sparse transform is used to sparsely represent the target scene and the image reconstruction is performed. The experimental results show that the proposed group DL based ISAR imaging method can provide better imaging results of the target scene than the existing DL based CS ISAR reconstruction algorithms with higher computational efficiency.