Fast ISAR Imaging Based on Enhanced Sparse Representation Model

Traditional sparse representation-(SR) based inverse synthetic aperture radar (ISAR) imaging schemes can achieve significant performance, but they suffer from high costs of memory and computational complexity, because the SR of a 2-D image is converted into that of a 1-D vector. Instead of memory consuming vector operations, we propose a fast ISAR imaging algorithm, where the decomposition and reconstruction of a 2-D scene is implemented using matrix operations directly. Besides the spatial sparsity of a scene, its structural sparsity is presented using the range profile of the scene, where both can be used to enhance sparsity exploitation during image reconstruction. Also, benefitting from the structural sparsity of the range profile, the target energy can be accumulated during the process, which further improves performance. Compared to available SR-based ISAR imaging algorithms, the proposed algorithm reduces both memory costs and computational complexity significantly, which is proven using simulated and real data.