A Novel Enhanced Convolutional Dictionary Learning Method for CS ISAR Imaging

Compressive sensing (CS) theory provides a positive contribution to ISAR imaging. However, the imaging performance of Compressive Sensing Inverse Synthetic Aperture Radar (CS ISAR) imaging methods is limited by the sparsity of the target scene. Dictionary learning (DicL) has been incorporated into CS ISAR imaging better to sparsify the target scene in a certain domain and improve imaging performance. However, the existing dictionary learning-based CS ISAR imaging methods are not adaptive enough and time-consuming. The algorithm parameters need to be manually adjusted for different targets. Their common iterative structure leads to relatively low computational efficiency. To improve the adaptive ability and computational efficiency of DicL-based CS ISAR imaging, we propose an enhanced convolutional DicL-based CS ISAR imaging method. Apart from exploiting the strong learning ability of the multi-layer network structure offered by the convolutional DicL, an attention mapping inferred in both spatial and channel dimensions and a multi-branch convolution are incorporated to enhance the sparsity in the latent space of the Convolutional DicL in CS ISAR imaging. The quantitative and qualitative analyses of the experimental results show that the proposed CS ISAR imaging method outperforms the existing DicL-based CS ISAR imaging methods and is also superior to the typical model-driven DL-based methods like ADMM-net.