深度神经网络在TIE波前探测中的应用  

Application of Deep Neural Network in Wavefront Sensing Based on Transport of Intensity Equation

作　　者：杨慧哲 张昊然 刘进[1,2] 万晶 赵路明 梁永辉[1,2] YANG Huizhe;ZHANG Haoran;LIU Jin;WAN Jing;ZHAO Luming;LIANG Yonghui(College of Advanced Interdisciplinary Studies,National University of Defense Technology,Changsha 410073,China;Nanhu Laser Laboratory,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学前沿交叉学科学院,长沙410073 [2]国防科技大学南湖之光实验室,长沙410073

出　　处：《光子学报》2024年第12期1-11,共11页Acta Photonica Sinica

基　　金：国家自然科学基金(No.62005314)。

摘　　要：提出了一种用于光强传输方程(TIE)波前探测的深度神经网络(DNN)训练模型。该模型的输入为两个不同传输距离的光强分布之差,输出为引起该光强变化的相位畸变对应的4~79阶Zernike系数。对比不同DNN模型下的波前重构精度,最终采用ResNet34骨干网络,提出用于颈网络的线性权重池化方法,并根据任务的物理背景设计了加权平均绝对误差损失函数。仿真结果表明,相较于传统的线性重构方法,DNN可以显著降低TIE波前探测对激光功率的要求,同时有效提升波前探测的精度。当激光功率为5 W时,DNN的探测精度相当于线性重构方法在激光功率为200 W时的水平,大大降低了对激光功率的要求(~40倍)。当激光功率超过20 W时,DNN的探测误差约为200 nm RMS,达到79阶Zernike多项式的探测精度上限。The Transport of Intensity Equation(TIE)offers an effective method for wavefront sensing,utilizing the variations in near-field defocused intensity distribution patterns across multiple propagation distances to reconstruct the phase aberrations introduced by turbulent media,such as the atmosphere.YANG Huizhe et al have explored TIE-based wavefront sensing for satellite-ground laser communication systems,addressing challenges related to the Point-Ahead Angle(PAA).Their simulations and bench experiments,using a Zernike-based linear reconstruction method,demonstrated effectiveness under high Signal-to-Noise Ratio(SNR)conditions.However,linear wavefront reconstruction faces significant nonlinear errors,rendering it ineffective in low SNR environments,which are common in low laser power scenarios typical of laser communication systems.To address these challenges,this paper proposes a Deep Neural Network(DNN)training model.The model utilizes the differences in intensity distributions observed at two distinct propagation distances as the input data.The outputs of the model are the first 4 to 79 orders of the Zernike coefficients corresponding to the phase aberrations.The input and output data used for DNN training are simulated through two processes based on the actual satellite-ground laser communication systems.The first process is the uplink propagation of a collimated laser beam through the atmospheric turbulence,while the second process is the reimaging of the backscattered patterns from these different altitudes.To generate a diverse set of datasets,three variable parameter sets are employed:the atmospheric coherence lengths of 0.05,0.10,and 0.15 meters;the turbulence layer heights of 0,5,and 10 kilometers;and the laser powers of 5,10,20,50,100,200,and 300 watts.This results in 63 unique combinations.Each combination contains 10,000 random phase screens,yielding a total of 630,000 training data.By comparing the Wavefront Errors(WFE)between the original and reconstructed phases,different model architectures,loss fu

关 键 词：波前探测 深度神经网络 光强传输 

分 类 号：O436[机械工程—光学工程]