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基于神经网络的消色差旋转双棱镜反向解算

罗昊鑫 姚宇翔 潘文武 游安清

罗昊鑫, 姚宇翔, 潘文武, 等. 基于神经网络的消色差旋转双棱镜反向解算[J]. 强激光与粒子束, 2023, 35: 071008. doi: 10.11884/HPLPB202335.220332
引用本文: 罗昊鑫, 姚宇翔, 潘文武, 等. 基于神经网络的消色差旋转双棱镜反向解算[J]. 强激光与粒子束, 2023, 35: 071008. doi: 10.11884/HPLPB202335.220332
Luo Haoxin, Yao Yuxiang, Pan Wenwu, et al. Inverse calculation of achromatic Risley prism based on neural network[J]. High Power Laser and Particle Beams, 2023, 35: 071008. doi: 10.11884/HPLPB202335.220332
Citation: Luo Haoxin, Yao Yuxiang, Pan Wenwu, et al. Inverse calculation of achromatic Risley prism based on neural network[J]. High Power Laser and Particle Beams, 2023, 35: 071008. doi: 10.11884/HPLPB202335.220332

基于神经网络的消色差旋转双棱镜反向解算

doi: 10.11884/HPLPB202335.220332
详细信息
    作者简介:

    罗昊鑫,874577946@qq.com

  • 中图分类号: O439

Inverse calculation of achromatic Risley prism based on neural network

  • 摘要: 为了实现基于双棱镜的光电跟踪平台,需要根据光束指向精确求解两棱镜转角。基于非近轴光线追迹法与二步法,利用神经网络拟合了消色差旋转双棱镜角度差值与偏转角之间的关系,并在求解方位角与消色差旋转双棱镜两组棱镜转角的时候,将其中的非线性关系与线性关系分开,利用神经网络对其中的非线性关系进行了拟合,最终得到了消色差旋转双棱镜转角与出射光束指向的数值关系。实验表明,在仅用3层神经网络共20个神经元的条件下,得到了两棱镜旋转角度反向解算值达到了 0.000 1°量级。
  • 图  1  消色差旋转双棱镜结构图

    Figure  1.  Achromatic Risley prisms structure

    图  2  消色差旋转双棱镜光路图

    Figure  2.  Optical path diagram for achromatic Risley prisms

    图  3  中心算法

    Figure  3.  Central algorithm

    图  4  偏转角与棱镜转角差绝对值的关系

    Figure  4.  Relationship between deflection angle and absolute value of prism angle difference

    图  5  神经网络结构图(偏转角)

    Figure  5.  Neural network structure diagram (deflection angle)

    图  6  方位角与两组棱镜转角的关系

    Figure  6.  Relationship between azimuth angle and two groups of prism angles

    图  7  消色差旋转双棱镜非线性部分

    Figure  7.  Nonlinear part for the achromatic Risley prisms

    图  8  消色差旋转双棱镜非线性部分与转角差的关系

    Figure  8.  Relationship between the angle difference and the nonlinear part

    图  9  神经网络结构图(非线性部分)

    Figure  9.  Neural network structure diagram (nonlinear part)

    图  10  方位角误差值

    Figure  10.  Deflection angle error value

    图  11  偏转角误差值

    Figure  11.  Azimuth error value

    图  12  消色差旋转双棱镜反向解算误差值

    Figure  12.  Reverse solution error value for achromatic risley prisms

    图  13  神经网络算法与二分法运算时间分析

    Figure  13.  Time analysis of neural network algorithm and dichotomy

    表  1  实验结果

    Table  1.   Results of experiments unit: (°)

    $ \phi $ $ {\mathit{\Theta}} $ $ {\theta }_{1}\left({\rm{real}}\right) $ $ {\theta }_{2}\left({\rm{real}}\right) $ $ {\theta }_{1}\left({\rm{fitting}}\right) $ $ {\theta }_{2}\left({\rm{fitting}}\right) $ $ {\theta }_{1}'\left({\rm{fitting}}\right) $ $ {\theta }_{2}'\left({\rm{fitting}}\right) $
    0.0186 90.0521 0.1 180 0.0973 180.0023 180.0072 0.1022
    0.3724 90.0424 1 179 1.0001 179.0004 179.0845 1.0842
    22.0821 4.3389 20 350 348.6781 18.6776 19.9998 350.0002
    17.5733 129.2439 90 166 90.0001 166.0000 168.4876 92.4878
    22.8874 325.2310 320 330 320.0006 329.9995 330.4613 320.4624
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-01-03
  • 修回日期:  2023-03-26
  • 录用日期:  2023-04-20
  • 网络出版日期:  2023-04-23
  • 刊出日期:  2023-06-15

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