摘要
光学合成孔径是研制大口径望远镜的有效技术途径。合成孔径光电望远镜实际分辨率能达到衍射极限的关键在于实时检测并校正各子孔径之间的平移误差。通过构建轻量化MobileNet卷积神经网络拟合宽波段点扩展函数和平移误差的非线性映射关系,并基于该网络完成了三孔径共相闭环实验。对三孔径合成孔径系统时序施加平移误差,采集相应宽波段点扩展函数,利用点扩展函数-平移误差数据训练轻量化MobileNet网络至收敛。在闭环校正阶段,将训练好的模型部署到嵌入式计算平台中,根据预测输出控制压电反射镜进行误差校正。共相闭环结果表明,该方法每次检测耗时3 ms,具有较好实时性,且能够在±6λ_(0)(λ_(0)=600 nm)的检测范围内,实现26.2 nm的检测精度。通过深度学习共相闭环实验,验证了深度学习方法作为工程级共相解决方案的可行性。
Objective Optical synthetic aperture is an effective technical approach for developing large aperture telescopes. The key to achieving diffraction limit for the actual resolution of synthetic aperture based opto-electronic telescopes lies in the real-time sensing and correction of piston error between sub-apertures. Among the traditional methods, the specific optics-based methods measure piston errors from the pupil information modulated by specially designed hardware, which inevitably increases the system complexity.The image-based methods can measure piston errors directly from the intensity image, which simplifies the system. However, it does need a large amount of iterative optimization calculation, thus failing to realize instant correction. Recently, deep learning method has contributed to many areas with piston sensing included, which is capable of achieving end-to-end piston sensing by fitting the mapping relationship between piston error and intensity image. Although many efforts have been made to improve the piston sensing performance of the deep learning model, most of the studies still stay in the simulation stage. In the few experimental studies, only piston sensing is implemented while co-phasing closed-loop correction has never been worked out. In the present study, we establish an optical synthetic aperture imaging experimental platform and implement co-phasing closed-loop experiment using deep learning approach. We hope that our research could be helpful for promoting the practical process of deep learning based co-phasing technology.Methods Real-time closed-loop piston error correction is achieved for two-aperture system and three-aperture system,respectively. First, the experimental platform is built, where broadband light is utilized to remove 2π ambiguity and sequence piston errors are loaded to the sub-apertures to generate corresponding training images. Then, a lightweight MobileNet convolutional neural network(CNN) is established to learn the nonlinear mapping relationship between broadba
作者
马霞飞
杨开元
马浩统
杨虎
谢宗良
Ma Xiafei;Yang Kaiyuan;Ma Haotong;Yang Hu;Xie Zongliang(National Key Laboratory of Optical Field Manipulation Science and Technology,Institute of Optics and Electronics,Chinese Academy of Sciences,Chengdu 610209,Sichuan,China;Key Laboratory of Optical Engineering,Chinese Academy of Sciences,Chengdu 610209,Sichuan,China;University of Chinese Academy of Sciences,Beijing 100049,China;School of Automation Engineering,University of Electronic Science and Technology of China,Chengdu 610209,Sichuan,China)
出处
《中国激光》
EI
CAS
CSCD
北大核心
2024年第13期288-296,共9页
Chinese Journal of Lasers
基金
国家自然科学基金(62005289,62175243)
中国科学院青年创新促进会项目(2020372)
国家重点研发计划(2022YFB3901900)。
关键词
成像系统
卷积神经网络
平移误差
光学合成孔径
共相闭环
imaging systems
convolutional neural network
piston error
optical synthetic aperture
cophasing closed loop
