A Deep Dictionary Learning Method for CS ISAR Imaging

The Compressive Sensing Inverse Synthetic Aperture Radar (CS ISAR) imaging methods are extremely beneficial for system simplification and reducing the data processing burden in ISAR imaging. Dictionary learning (DicL)-based methods have somewhat extended the initial requirement of sparsity in a transformed domain to more general low-dimensional latent representations. However, the high computational complexity and manual tuning of parameters in the DicL-based method result in repetitive optimization operations needed when processing different data, which is time-intensive. To improve the efficiency and flexibility of CS ISAR imaging, we propose a deep dictionary learning method for CS ISAR imaging, which combines DicL with deep learning (DL) techniques to realize high-quality adaptive imaging with incomplete data. The proposed method unfolds the DicL process into a cascade network and optimizes a transform dictionary and sparse representation coefficients adaptively according to the prior information of the target. The qualitative and quantitative analyses of the experimental results show that the proposed method is superior to the traditional imaging method and can be effectively used for ISAR high-resolution imaging under incomplete data.