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深度学习波面传感来自对象场景的定向能源HEL系统.

Leonardo Herrera1, Nicholas Messina1, Brij N Agrawal1

  • 1Department of Mechanical and Aerospace Engineering, Naval Postgraduate School, 1 University Circle, Monterey, CA 93943, USA.

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概括
此摘要是机器生成的。

本研究引入了深度学习 (DL) 方法,用于高能激光 (HEL) 系统的波面传感,从而消除了对传统传感器的需求. 该DL模型准确地从无人机图像中预测大气动荡,甚至超出训练参数.

关键词:
适应式光学适应式光学卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.沙克 - 哈特曼传感器传感器波面错误 波面错误 波面错误 波面错误泽尼克系数的使用方法

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科学领域:

  • 光学工程是指光学工程.
  • 人工智能的人工智能
  • 大气物理学 大气物理学

背景情况:

  • 大气流会扭曲高能激光 (HEL) 波浪,降低系统性能.
  • 传统的自适应光学 (AO) 系统需要波面传感器和参考束,增加复杂性和成本.

研究的目的:

  • 开发基于深度学习 (DL) 的波浪前线传感方法,只使用场景图像.
  • 消除在HEL系统中需要专门的波面传感器和参考光束.

主要方法:

  • 一个DL模型经过训练,可以从Reaper无人机 (UAV) 的异常图像中预测泽尼克系数 (波面扭曲).
  • 该模型在图像中训练了具有不同流水平 (D/r0) 的图像,并对未见的流水平和不同无人机类型 (Mongoose) 的概括进行了测试.

主要成果:

  • 在多个流级别中训练的DL模型在准确性方面超过了单级训练模型.
  • 该模型表现出强烈的概括性,准确地预测了超出其训练范围的流水平和蒙古斯无人机图像的流.

结论:

  • 基于DL的波面传感是一种可行的传统AO的替代方案,可降低复杂性和成本.
  • 拟议的DL模型为在大气条件下运行的HEL系统提供了准确的流预测和强大的概括.