Estimation of atmospheric turbulence intensity based on deep learning

Shengjie Ma; Shiqia Hao; Qingsong Zhao; Chenlu Xu

doi:10.1117/12.2603106

31 August 2021 Estimation of atmospheric turbulence intensity based on deep learning

Shengjie Ma, Shiqia Hao, Qingsong Zhao, Chenlu Xu

Proceedings Volume 11907, Sixteenth National Conference on Laser Technology and Optoelectronics; 1190726 (2021) https://doi.org/10.1117/12.2603106
Event: Sixteenth National Conference on Laser Technology and Optoelectronics, 2021, Shanghai, China

Abstract

The free space optical communication system will inevitably be affected by atmospheric turbulence when working, which will increase the bit error rate at the receiving end, and seriously affects the communication quality, so it is necessary to analyze the characteristics of atmospheric turbulence. In the paper, the powerful feature extraction and data processing capabilities of the convolutional neural network is used to estimate the atmospheric turbulence refractive index structure constant C²_n , and the influence of transmission distance, beam multiplexing technology and beam mode is analyzed on the estimation effect. The results show that this method can effectively estimate C²_n , and the error can be controlled within a reasonable range.

Citation Download Citation

Shengjie Ma, Shiqia Hao, Qingsong Zhao, and Chenlu Xu "Estimation of atmospheric turbulence intensity based on deep learning", Proc. SPIE 11907, Sixteenth National Conference on Laser Technology and Optoelectronics, 1190726 (31 August 2021); https://doi.org/10.1117/12.2603106

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