基于深度卷积神经网络的大气湍流强度估算

提出了一种基于深度卷积神经网络估算大气湍流折射率结构常数C_n²的方法。将湍流影响下的高斯光束光斑图像作为神经网络的输入,利用深度卷积神经网络提取图像的特征信息,得到C_n²大小,并采用平均绝对误差、平均相对误差、均方根方差和相关系数四个统计量来衡量模型的估算效果。结果表明,该模型能够根据湍流影响下的高斯光束光斑图像对C_n²进行估算,当迭代500次时,相关系数为99.84%,各项误差均在2%左右。该模型在大气湍流特性分析及大气湍流强度估算等领域有一定应用价值。

Atmospheric Turbulence Intensity Estimation Based on Deep Convolutional Neural Networks

Objective Atmospheric turbulence causes a random fluctuation in the refractive index.When a laser propagates in atmospheric turbulence,the light intensity fluctuation phenomenon during beam propagation occurs,seriously influencing laser propagation.Because different atmospheric turbulence intensities have different effects on laser propagation,it is significantly important to estimate the atmospheric turbulence intensity.In general,the refractive index structural constant C_n² of the atmospheric turbulence is used to measure the turbulence intensity.The value of C_n² is directly proportional to the impact of turbulence on laser propagation.Traditional estimation methods include instrument measurement and model estimation.The instrument measurement allows building an experimental platform to directly measure C_n²,in contrast,the model estimation allows obtaining C_n² by measuring other atmospheric parameters and establishing a model.In recent years,deep learning has allowed achieving good results in the field of image processing,which can extract the feature information of an image layer by layer.This study proposes a method to estimate the refractive index structural constant C_n² of atmospheric turbulence based on deep convolutional neural networks.The neural network model is built to extract the features of the light spot images under the influence of atmospheric turbulence and the turbulence information is obtained to estimate the turbulence intensity.Results and Discussions In this study,a traditional AlexNet network model and a VGG16 deep convolutional neural network model are established.VGG16 is optimized on the basis of the traditional convolutional neural network,which increases the layer numbers of the network,reduces the size of the convolution kernel,and has more advantages on feature information extraction of images.The light spot images at different moments under the same turbulence intensity are selected as the inputs of the neural network to verify the feasibility of the above mentioned method and obtain the corresponding estimation results.Moreover,the standard deviation is calculated,and the estimation results are analyzed.The results show that the method can well estimate the turbulence intensity,and the standard deviation increases with the turbulence intensity.To better analyze the results of the neural network model and measure the estimation results,four statistics,i.e.,mean absolute error(EMAE),mean relative error(EMRE),root-mean-square variance(ERMSE),and correlation coefficient(Rxy),are selected.The spot images under the influences of different turbulence intensities are randomly selected as the inputs of the neural network model to obtain the corresponding output.The estimation results of the two neural network models are shown in Table 5.After 20 iterations,the estimation result of the VGG16 neural network model is relatively ideal,the correlation coefficient reaches 99%,and EMAE,EMRE,and ERMSEare controlled within 5%.After 500 iterations,EMAE,EMRE,and ERMSEare further reduced to 2%.By analyzing Table 5,it can be seen that both models can well estimate the turbulence intensity after 500 iterations,and the estimation effect of VGG16 is better than that of the AlexNet neural network model.When the number of iterations is the same,EMAE,EMRE,and ERMSEestimated by the VGG16 neural network model are less than half of those of the AlexNet neural network model.Compared with the traditional AlexNet neural network model,the VGG16 neural network model optimizes the network structure and improves the estimation effect to a certain extent.Conclusion In this study,a method based on deep convolutional neural network model is proposed to estimate turbulence intensity.First,the laser spot images under the influence of turbulence can be simulated according to the classical phase screen theory.Then,the laser spot images under the influence of turbulence are taken as the inputs of the deep convolutional neural network model,and the convolutional layer of the deep convolutional neural network model is used to extract feature information of images layer by layer.After the training of a large number of datasets,the network model is obtained,and the turbulence intensity is estimated.Finally,the estimated effect is analyzed.Compared with the traditional AlexNet neural network model,the VGG16 model adopts a small convolution kernel,which can better retain the image properties,and has high advantages on image feature extraction and better estimation effect.Therefore,the neural network model can be further optimized to improve the estimation effect,which provides a new way to estimate turbulence intensity.