Atmospheric Turbulence Strength Estimation Using Convolution Neural Network
Laser beam transmission in atmospheric turbulence causes image distortion and affects the quality of information transmission in the field of optical communication. The strength of the atmospheric turbulence, which can be characterized by refractive index structure constant <inline-formula><...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10250932/ |
| _version_ | 1797656270722826240 |
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| author | Siyu Gao Xiaoyun Liu Yonghao Chen Jinyang Jiang Ying Liu Yueqiu Jiang |
| author_facet | Siyu Gao Xiaoyun Liu Yonghao Chen Jinyang Jiang Ying Liu Yueqiu Jiang |
| author_sort | Siyu Gao |
| collection | DOAJ |
| description | Laser beam transmission in atmospheric turbulence causes image distortion and affects the quality of information transmission in the field of optical communication. The strength of the atmospheric turbulence, which can be characterized by refractive index structure constant <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula>, significantly influences the properties of a laser beam. The accurate estimation of <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula> is essential for understanding the strength of turbulence. Although multilayer perceptron (MLP) and deep neural network (DNN) has been applied to estimate the atmospheric turbulence strength, the estimation accuracy is sensitive to the strength of the turbulence. In this article, we propose a method based on the convolution neural network (CNN) approach to estimate <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula> ranging from <inline-formula><tex-math notation="LaTeX">$10^{-17}$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$10^{-13}$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$\text{m}^{-2/3}$</tex-math></inline-formula>. We experimentally demonstrate that the correlation coefficient (<inline-formula><tex-math notation="LaTeX">$\rm R^{2}$</tex-math></inline-formula>) of the model is 99.39%. The mean relative error (MRE), root mean square error (RMSE), and mean absolute error (MAE) are 0.0047, 0.0916, and 0.0684, respectively. For the turbulence strength with the same order of refractive index structure constant <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula>, the estimation accuracy of the weak turbulence is higher than that of medium and strong turbulence. Moreover, the mix training different levels of turbulence strength improves the estimation accuracy of <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula> compared to that with the same order of <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula>. Based on the high estimation accuracy of the CNN in the scheme, the proposed method will be able to provide a way of estimating the strength of atmospheric turbulence without the need for additional optical devices. |
| first_indexed | 2024-03-11T17:27:02Z |
| format | Article |
| id | doaj.art-b5ab389054be4b418b8602568e070e46 |
| institution | Directory Open Access Journal |
| issn | 1943-0655 |
| language | English |
| last_indexed | 2024-03-11T17:27:02Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj.art-b5ab389054be4b418b8602568e070e462023-10-18T23:00:13ZengIEEEIEEE Photonics Journal1943-06552023-01-011561710.1109/JPHOT.2023.331483310250932Atmospheric Turbulence Strength Estimation Using Convolution Neural NetworkSiyu Gao0https://orcid.org/0009-0001-5210-6435Xiaoyun Liu1https://orcid.org/0000-0003-0480-3882Yonghao Chen2https://orcid.org/0009-0001-3737-2815Jinyang Jiang3https://orcid.org/0009-0001-8192-5886Ying Liu4https://orcid.org/0009-0008-7981-1362Yueqiu Jiang5https://orcid.org/0000-0002-2519-0926School of Science, Shenyang Ligong University, Shenyang, ChinaSchool of Science, Shenyang Ligong University, Shenyang, ChinaSchool of Science, Shenyang Ligong University, Shenyang, ChinaSchool of Science, Shenyang Ligong University, Shenyang, ChinaSchool of Science, Shenyang Ligong University, Shenyang, ChinaDepartment of Development and Planning, Shenyang Ligong University, Shenyang, ChinaLaser beam transmission in atmospheric turbulence causes image distortion and affects the quality of information transmission in the field of optical communication. The strength of the atmospheric turbulence, which can be characterized by refractive index structure constant <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula>, significantly influences the properties of a laser beam. The accurate estimation of <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula> is essential for understanding the strength of turbulence. Although multilayer perceptron (MLP) and deep neural network (DNN) has been applied to estimate the atmospheric turbulence strength, the estimation accuracy is sensitive to the strength of the turbulence. In this article, we propose a method based on the convolution neural network (CNN) approach to estimate <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula> ranging from <inline-formula><tex-math notation="LaTeX">$10^{-17}$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$10^{-13}$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$\text{m}^{-2/3}$</tex-math></inline-formula>. We experimentally demonstrate that the correlation coefficient (<inline-formula><tex-math notation="LaTeX">$\rm R^{2}$</tex-math></inline-formula>) of the model is 99.39%. The mean relative error (MRE), root mean square error (RMSE), and mean absolute error (MAE) are 0.0047, 0.0916, and 0.0684, respectively. For the turbulence strength with the same order of refractive index structure constant <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula>, the estimation accuracy of the weak turbulence is higher than that of medium and strong turbulence. Moreover, the mix training different levels of turbulence strength improves the estimation accuracy of <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula> compared to that with the same order of <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula>. Based on the high estimation accuracy of the CNN in the scheme, the proposed method will be able to provide a way of estimating the strength of atmospheric turbulence without the need for additional optical devices.https://ieeexplore.ieee.org/document/10250932/Atmospheric turbulenceconvolution neural networkrefractive index structure constant <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$C^{2}_{n}$</tex-math> </inline-formula> </named-content> |
| spellingShingle | Siyu Gao Xiaoyun Liu Yonghao Chen Jinyang Jiang Ying Liu Yueqiu Jiang Atmospheric Turbulence Strength Estimation Using Convolution Neural Network IEEE Photonics Journal Atmospheric turbulence convolution neural network refractive index structure constant <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$C^{2}_{n}$</tex-math> </inline-formula> </named-content> |
| title | Atmospheric Turbulence Strength Estimation Using Convolution Neural Network |
| title_full | Atmospheric Turbulence Strength Estimation Using Convolution Neural Network |
| title_fullStr | Atmospheric Turbulence Strength Estimation Using Convolution Neural Network |
| title_full_unstemmed | Atmospheric Turbulence Strength Estimation Using Convolution Neural Network |
| title_short | Atmospheric Turbulence Strength Estimation Using Convolution Neural Network |
| title_sort | atmospheric turbulence strength estimation using convolution neural network |
| topic | Atmospheric turbulence convolution neural network refractive index structure constant <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$C^{2}_{n}$</tex-math> </inline-formula> </named-content> |
| url | https://ieeexplore.ieee.org/document/10250932/ |
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