Chinese Journal of Lasers, Volume. 48, Issue 4, 0401018(2021)

Atmospheric Turbulence Intensity Estimation Based on Deep Convolutional Neural Networks

Shengjie Ma1,2, Shiqi Hao1,2、*, Qingsong Zhao1,2, Yong Wang1,2, and Lei Wang1,2
Author Affiliations
  • 1State Key Laboratory of Pulse Power Laser Technology, National University of Defense Technology, Hefei, Anhui 230037, China
  • 2AnHui Province Key Laboratory of Electronic Restriction, Hefei,Anhui 230037, China
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    References(20)

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    [5] C A. La Rue J C, Champagne F H, et al. Effects of temperature and humidity fluctuations on the optical refractive index in the marine boundary layer[J]. Journal of the Optical Society of America, 65, 1502-1511(1975).

    [6] Davidson K L, Schacher G E, Fairall C W et al. Verification of the bulk method for calculating overwater optical turbulence[J]. Applied Optics, 20, 2919-2924(1981).

    [11] Qing C, Wu X Q, Li X B et al. Use of weather research and forecasting model outputs to obtain near-surface refractive index structure constant over the ocean[J]. Optics Express, 24, 13303-13315(2016).

    [12] Wang Y, Basu S. Using an artificial neural network approach to estimate surface-layer optical turbulence at Mauna Loa, Hawaii[J]. Optics Letters, 41, 2334-2337(2016).

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    Shengjie Ma, Shiqi Hao, Qingsong Zhao, Yong Wang, Lei Wang. Atmospheric Turbulence Intensity Estimation Based on Deep Convolutional Neural Networks[J]. Chinese Journal of Lasers, 2021, 48(4): 0401018

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    Paper Information

    Special Issue: SPECIAL ISSUE FOR "NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY"

    Received: Jun. 28, 2020

    Accepted: Aug. 10, 2020

    Published Online: Feb. 8, 2021

    The Author Email: Hao Shiqi (liu_hsq@126.com)

    DOI:10.3788/CJL202148.0401018

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