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无信标的适应光学用于大气激光传播与多平面卷积神经网络
Optics express
|July 30, 2025
概括
我们开发了一种机器学习方法来模拟适应光学,用于在没有信标的情况下进行激光传播. 这种技术通过分析散射光来提高光束质量,为没有信标的自适应光学提供解决方案.
科学领域:
- 光学是什么?光学是什么?光学是什么?
- 机器学习 机器学习
- 激光物理 激光物理
背景情况:
- 适应光学 (AO) 系统纠正由大气流引起的波面扭曲.
- 传统的AO需要一个信标激光来进行波面传感,这并不总是可用的.
- 激光传播通过散射介质对AO校正提出了挑战.
研究的目的:
- 开发一种基于机器学习 (ML) 的方法来模拟激光传播中的自适应光学 (AO).
- 为了在没有专用信标激光器的场景中实现 AO 校正.
- 为了提高激光束通过散射介质传播的功绩数字.
主要方法:
- 利用卷积神经网络 (CNN) 将散射光强度配置文件与相位配置文件相关联.
- 开发了一种ML方法来模拟没有信标激光的AO动作.
- 在图像平面中记录的散射光强度概况与对象平面接近并联.
主要成果:
- 基于ML的AO仿真与单独的倾斜修正相比,取得了更好的优点.
- 通过散射介质证明了无信标的OA在激光传播中的可行性.
- 量化了开发的技术所取得的性能改善.
结论:
- 开发的ML方法有效地模拟了AO在没有信标的情况下进行激光传播.
- 这种技术在缺乏信标的散射环境中为AO校正提供了可行的解决方案.
- 该研究强调了ML在推进激光传播的AO应用中的潜力.
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