基于深度学习的拼接镜共相检测新方法研究  

Research on New Co-phasing Detection Method of Segmented Mirror Based on Deep Learning

作　　者：李斌[1] 杨阿坤 孙赵祥 陈楠 Li Bin;Yang Akun;Sun Zhaoxiang;Chen Nan(Intelligent Electromechanical Equipment Innovation Research Institute,East China Jiaotong University,Nanchang 330013,Jiangxi,China)

机构地区：[1]华东交通大学机智能机电装备创新研究院,江西南昌330013

出　　处：《中国激光》2023年第22期72-80,共9页Chinese Journal of Lasers

基　　金：国家自然科学基金(12103019)。

摘　　要：基于互相关算法的双波长共相检测方法在大量程共相误差检测中,存在检测速度慢、精度低的问题。针对该问题,利用卷积神经网络的方法建立拼接镜的平移(piston)误差预测模型,以实现双波长共相检测方法在大量程共相误差下的快速、准确检测。首先,将两波长下的圆孔衍射图像拼接作为卷积神经网络的训练数据。训练结束后,将包含piston误差信息的圆孔衍射拼接图像输入到训练好的模型中,可直接检测出piston误差值。仿真结果表明:基于卷积神经网络的共相方法具有高的检测精度、快的检测速度及较好的抗噪性和泛化能力。该方法为平移误差的测量提供了一种可行且易于实现的方案。Objective A large aperture telescope is needed to achieve long distance observations.The size of a single aperture telescope is limited by processing costs and other factors,and the segmented mirror technology is expected to break through the single aperture telescope limit.The key to the realization of segmented mirror technology is fine co-phasing.Currently,the most widely used technique for co-phasing detection is the broadband and narrowband Shack-Hartmann(S-H)method.The broadband S-H detection range is large,but the accuracy is low(30 nm),whereas the narrowband S-H method has a high detection accuracy of 6 nm;however,there is 2πambiguity effect and its detection range isλ/2.The conventional cross-correlation algorithm uses two wavelengths to detect the co-phasing error,which effectively solves the 2πambiguity effect in single wavelength detection and simultaneously improves the detection range.In this study,to address the slow detection speed and low accuracy of the current two-wavelength co-phasing detection method using the cross-correlation algorithm in the detection of large-range co-phasing errors,a two-wavelength co-phasing algorithm based on convolutional neural networks is proposed to achieve fast and accurate co-phasing detection in large-range cophasing errors.First,the circular diffraction image splicing at the two wavelengths is used as the training data for the convolutional neural network.After training,the circular diffraction splicing image containing the piston error information is input into the trained model,and the piston error value is detected directly.The robustness of the convolutional network based on convolutional networks under different error situations is also analyzed.Methods Based on the principle of circular diffraction,the circular diffraction pattern with the piston error information is first obtained through software simulation,and the circular diffraction patterns corresponding to the piston error at the two wavelengths are used to splice and obtain the data set for tra

关 键 词：测量 卷积神经网络 piston误差 拼接镜 圆孔衍射 

分 类 号：O436[机械工程—光学工程]