Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns

Author(s)

Artem V. Vorontsov, AI Science & Technology LLCFollow
Mikhail A. Vorontsov, University of DaytonFollow
Grigorii A. Fillimonov, University of DaytonFollow
Ernst Polnau (0000-0002-0979-8712), University of DaytonFollow

Document Type

Article

Publication Date

11-17-2020

Publication Source

Applied Sciences-Basel

Abstract

A new paradigm for machine learning-inspired atmospheric turbulence sensing is developed and applied to predict the atmospheric turbulence refractive index structure parameter using deep neural network (DNN)-based processing of short-exposure laser beam intensity scintillation patterns obtained with both: experimental measurement trials conducted over a 7 km propagation path, and imitation of these trials using wave-optics numerical simulations. The developed DNN model was optimized and evaluated in a set of machine learning experiments. The results obtained demonstrate both good accuracy and high temporal resolution in sensing. The machine learning approach was also employed to challenge the validity of several eminent atmospheric turbulence theoretical models and to evaluate them against the experimentally measured data.

ISBN/ISSN

2076-3417

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.3390/app10228136

Publisher

MDPI

Volume

10

Issue

22

Peer Reviewed

yes

eCommons Citation

Vorontsov, Artem V.; Vorontsov, Mikhail A.; Fillimonov, Grigorii A.; and Polnau, Ernst, "Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns" (2020). Electro-Optics and Photonics Faculty Publications. 141.
https://ecommons.udayton.edu/eop_fac_pub/141