Accurately reconstructing synthetic aperture radar (SAR) images from incomplete echo measurements is the fundamental problem in simplifying SAR systems and reducing sensing costs. In solving this problem, the recently emerged deep learning SAR imaging concept shows high potential in offering both high accuracy and efficiency. However, such approaches face challenges in network topology designing, weights initialization, and dataset construction, which hinder their practical deployments. To address these problems, we propose a generative learning imaging framework, namely, generative learning imaging framework (GLIm), for millimeter wave (mmWave) SAR in tasks of sparsely sensing scenarios. In our scheme, the targeting images are generated from a specifically designed SAR image generator by mapping low-dimensional random noise to the scattering domain. In addition, the SAR image generator is trained based on a compound loss function where the incomplete echo measurements serve as the supervised signal. Unlike traditional algorithms, the algorithm proposed in this article solves the optimal solution purely through numerical propagation by online learning, avoiding the theoretical derivation of the iterative optimization process. Finally, experiments are carried out on both simulated and real-measured data, and both numerical and visual results demonstrate the viability of the proposed GLIm in the tasks of reconstructing mmWave SAR images from sparsely sampled echo measurements.
Wang et al. (Thu,) studied this question.