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$Influence of Earth's Curvature and Atmospheric Refraction on Leveling$ 01:26

Influence of Earth's Curvature and Atmospheric Refraction on Leveling

During leveling, the Earth's curvature and atmospheric refraction introduce deviations in the line of sight from a true horizontal reference. When the line of sight is leveled, it remains perpendicular to the plumb line only at a single point. Beyond this, it deviates due to the Earth’s curvature, represented by the correction C. For a sight distance D, the deviation can be derived using the relationship:This relationship shows that the deviation increases quadratically with distance.

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Bringing the Visible Universe into Focus with Robo-AO

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基于深度学习的大气流偏差校正,基于深度学习的波面传感.

Jiang You^1,2, Jingliang Gu¹, Yinglei Du¹

¹Institute of Applied Electronics, China Academy of Engineering Physics, Mianyang 621900, China.

Sensors (Basel, Switzerland)

|November 25, 2023

概括

一个新的深度学习波面传感 (DLWS) 模型,使用注意力机制和CNN,准确地测量大气流. 这种DLWS系统显著提高了激光束强度超过千米距离,而无需重新训练.

关键词:

在美国,CNN是CNN.异常纠正实验的异常纠正实验适应式光学 (AO) 适应式光学注意力机制注意力机制深度学习波浪前传感 (DLWS)

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Last Updated: Jul 10, 2025

Bringing the Visible Universe into Focus with Robo-AO

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Published on: February 12, 2013

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

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Published on: April 12, 2014

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

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科学领域:

光学和光子学在光学和光子学.
人工智能的人工智能
大气科学大气科学

背景情况:

波面传感对于自适应光学系统来纠正光学偏差至关重要.
像Shack-Hartmann波面传感 (SHWS) 这样的传统方法在速度和复杂性方面都有局限性.
深度学习为实时波面测量和校正提供了一个有希望的替代方案.

研究的目的:

开发和验证一个新的深度学习波面传感 (DLWS) 系统.
对DLWS模型的性能进行评估,并将其与SHWS等既有方法进行比较.
为了证明DLWS在远程激光偏差校正中的有效性.

主要方法:

开发一个DLWS神经网络,整合注意力机制和卷积神经网络 (CNN).
使用模拟大气流数据集和内部实验平台进行培训和验证.
在千米范围激光传输实验中部署和测试训练的DLWS模型.

主要成果:

在室内实验中,DLWS模型的准确性与Shack-Hartmann波面传感 (SHWS) 方法相美.
在室内训练的DLWS模型直接应用于千米级实验,而无需重新训练.
使用DLWS的闭环校正导致激光点在目标上的平均峰值强度增加了5.35倍.

结论:

拟议的DLWS模型证明了大气流补偿的高精度和稳定性.
在远程光学系统中,DLWS提供了一种实用且高效的解决方案,用于实时波面传感和偏差校正.
该研究强调了深度学习在推进自适应光学和激光通信技术方面的潜力.