Oceanographic machine learning for free-space optical performance prediction

2020 ◽  
Author(s):  
Burton Neuner ◽  
Adrian Mai ◽  
Jonathan Itschner ◽  
Skylar Lilledahl ◽  
Ben Laxton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Prediction ◽  
Free Space ◽  
Optical Performance ◽  
Free Space Optical

A machine learning approach to array-based free-space optical communications

2021 ◽  
Author(s):  
James Miller ◽  
Paul Keeley ◽  
Peter Ateshian ◽  
Jerome Nilmeier ◽  
Nur Dwijayanto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Communications ◽  
Free Space ◽  
Learning Approach ◽  
Free Space Optical ◽  
Machine Learning Approach

scholarly journals An Improved Method of Measuring Wavefront Aberration Based on Image with Machine Learning in Free Space Optical Communication

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3665 ◽  
Author(s):  
Yangjie Xu ◽  
Dong He ◽  
Qiang Wang ◽  
Hongyang Guo ◽  
Qing Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Communication ◽  
Free Space ◽  
Computation Time ◽  
Estimation Accuracy ◽  
Wavefront Aberration ◽  
Free Space Optical Communication ◽  
Free Space Optical ◽  
Improved Method ◽  
Space Optical Communication

In this paper, an improved method of measuring wavefront aberration based on image with machine learning is proposed. This method had better real-time performance and higher estimation accuracy in free space optical communication in cases of strong atmospheric turbulence. We demonstrated that the network we optimized could use the point spread functions (PSFs) at a defocused plane to calculate the corresponding Zernike coefficients accurately. The computation time of the network was about 6–7 ms and the root-mean-square (RMS) wavefront error (WFE) between reconstruction and input was, on average, within 0.1263 waves in the situation of D/r0 = 20 in simulation, where D was the telescope diameter and r0 was the atmospheric coherent length. Adequate simulations and experiments were carried out to indicate the effectiveness and accuracy of the proposed method.

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