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基于卷积神经网络的大气中光路气流扰动实验研究
Atmospheric Optical Path Airflow Disturbance Analysis Method Based on Convolutional Neural Network
【摘要】 提出了一种基于激光光斑畸变和卷积神经网络(CNN)的光路气流扰动研究方案。利用CNN对激光光束在空间传播中受到气流扰动后的光斑畸变进行学习,得到光束传播路径上的气流扰动情况。实验表明,训练得到的评估参数与由风速仪测得的光路中的气流扰动(风速)具有强相关性。本方案提供了一种短距离、快速、低成本的气流扰动分析手段。
【Abstract】 A method to investigate optical path turbulence based on laser spot distortion and a convolutional neural network(CNN) is proposed. Utilizing the CNN, we evaluated the spot distortion of laser beams resulting from airflow disturbance in space propagation. As a result, details of turbulence on the beam propagation path can be obtained. Experimental results demonstrate a high correlation between the evaluation parameter and the turbulent intensity(wind speed) measured by an anemoscope. The proposed method provides a turbulence analysis with short distance, high speed, and low cost.
【Key words】 atmospheric optics; free space optics; air flow disturbance; convolution neural network; deep learning;
- 【文献出处】 光学学报 ,Acta Optica Sinica , 编辑部邮箱 ,2019年08期
- 【分类号】TP183
- 【网络出版时间】2019-04-16 17:45
- 【下载频次】143