A Survey of Synthetic Aperture Radar Imaging Methods Based on Deep Learning

‍ ‍Modern Synthetic Aperture Radar （SAR） systems operate in increasingly complex electromagnetic environments， requiring higher imaging accuracy， real-time capability， and algorithm robustness. Traditional matched filtering and compressed sensing technology have apparent limitations in meeting the high-standard requirements of SAR imaging， especially in imaging efficiency and resolution. With the rapid development of machine learning， researchers combine deep learning networks with radar imaging algorithms and propose learning imaging technology， aiming to find new solutions for high-quality real-time imaging. This paper introduces the deep learning network architecture used to solve the inverse problem of SAR imaging from two perspectives of data-driven and the combination of model-driven and data-driven. On this basis， the development and research status of SAR stationary target learning imaging， SAR moving target learning imaging， 3D-SAR learning imaging， and ISAR learning imaging are reviewed to help researchers and practitioners understand the application of deep learning technology to SAR imaging-related issues. Finally， some unresolved problems are proposed， while discussing potential solutions and future trends.