A Composite Network for CS ISAR Integrating Deep Adaptive Sampling and Imaging

Compressive sensing (CS) actively contributes to inverse synthetic aperture radar (ISAR) imaging with less raw data. The design of the measurement matrix and the development of reconstruction methods are critical processes in CS ISAR imaging. However, the existing CS ISAR imaging methods based on deep learning (DL) mainly focus on improving the performance of the reconstruction algorithm while ignoring the potential room for improvement given by the design of the measurement matrix. To take full advantage of the compression potential of the measurement matrix, we propose a CS ISAR imaging technique based on adaptive sampling, utilizing DL to learn a priori information about the target scene and designing an optimal sampling strategy that uses less data to achieve high-quality imaging. Furthermore, we integrate CS ISAR imaging into a composite network, in which the sampling and reconstruction stage is optimized globally, realizing deep adaptive sampling imaging with a high compression ratio. The CS ISAR imaging with adaptive sampling consists of sampling and reconstruction networks, where the sampling network compresses the radar data by a convolutional neural network, and the reconstruction network mainly performs the image reconstruction by convolutional dictionary learning. In addition, we adopt the block-based CS method in the sampling network to alleviate the computational burden caused by vectorizing and stacking the data and introduce a nonlocal self-similarity model into the reconstruction network to improve the imaging quality. The qualitative and quantitative analysis of the experiments on real data demonstrates that the novel method can achieve higher quality ISAR imaging than other nonadaptive sampling methods at a low sampling ratio, demonstrating its superiority.