Single Ended In-Situ Atmospheric Turbulence Strength Characterization Using Deep Neural Networks
Date of Award
5-1-2025
Degree Name
M.S. in Electro-Optics
Department
Department of Electro-Optics and Photonics
Advisor/Chair
Mikhail Vorontsov
Abstract
This thesis explores a AI-based approach for single-ended prediction of atmospheric turbulence strength at high temporal rates using Deep Neural Network (DNN) models trained on computer simulated images of laser beam intensity scintillation patterns. The major objective of this study is to investigate whether a DNN model trained solely on intensity scintillation patterns obtained using unidirectional (single-pass) laser beam propagation can accurately predict strength of atmospheric turbulence values in the target-in-the-loop (double-pass) propagation scenarios with laser beam scattering off a retro-target or a target glint. The obtained results demonstrate ability of accurate prediction of atmospheric turbulence strength by the DNN model trained on the single pass data in double-pass operational engagements for relatively small size retro-targets under wide range of turbulence conditions.
Keywords
Artificial Intelligence, Energy, Engineering, Optics
Rights Statement
Copyright 2025, author.
Recommended Citation
Saggu, Prabjeet, "Single Ended In-Situ Atmospheric Turbulence Strength Characterization Using Deep Neural Networks" (2025). Graduate Theses and Dissertations. 7535.
https://ecommons.udayton.edu/graduate_theses/7535
