基于深度学习的合成孔径成像系统共共相误差检测研究综述

Review of co-phasing error detection for synthetic aperture imaging system based on deep learning

光学合成孔径成像系统是利用多个小口径望远镜排列成稀疏孔径阵列来增大系统等效口径,从而实现大口径光学系统的高分辨成像效果。子孔径间的共相误差探测是实现合成孔径系统高分辨率成像的重要前提,该技术一直是该领域研究人员关注的焦点之一。新兴的人工智能及大数据技术为合成孔径成像系统共相误差探测提供了新思路和开辟了新方向。本文在简要回顾合成孔径成像系统共相误差探测方法的基础上,介绍和分析了近年来深度学习技术在合成孔径成像系统共相误差探测方面的研究进展,并对未来发展方向进行了总结和展望。

In the optical synthetic aperture imaging system,multiple small aperture telescopes are arranged into a sparse aperture array to increase the equivalent aperture of the system,so as to achieve the high-resolution imaging effect of the large aperture optical system.The detection of co-phasing error between subapertures is an important prerequisite for realizing high-resolution imaging of synthetic aperture systems,and this technology has always been one of the focuses of researchers in this field.The emerging artificial intelligence and big data technology provide a new idea and open up a new direction for the detection of co-phasing error of synthetic aperture imaging system.On the basis of a brief review of the co-phasing error detection methods of synthetic aperture imaging system,the research progress of deep learning technology in co-phasing error detection of synthetic aperture imaging system in recent years is introduced and analyzed,and the future development direction is finally summarized and prospected.