MEASUREMENTS OF OPTICAL TURBULENCE AND ANALYSIS USING MACHINE LEARNING
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Authors
Sklavounos, Antonios
Subjects
turbulence
machine learning
laser energy
laser propagation
machine learning
laser energy
laser propagation
Advisors
Blau, Joseph A.
Cohn, Keith R.
Date of Issue
2021-12
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Optical turbulence impacts the performance of laser weapons and laser communication by disrupting the focus of the laser beam. It is important to characterize the turbulence along the beam path in order to predict the performance of these systems. Unfortunately, the equipment needed to measure optical turbulence is delicate. A previous thesis found that the turbulence can be estimated using machine learning regression analysis trained on simple atmospheric measurements that can be made with more robust instruments. Machine learning regression analysis is a powerful tool to model complex phenomena with no clear analytical relationship, although extensive data sets are required to train the machine learning model. For this thesis, we measured optical turbulence and various atmospheric parameters (air temperature, humidity, solar flux, etc.) over many months. Using measured atmospheric parameters as inputs, we developed an ensemble of bagged trees regression model with optical turbulence as the response. Overall, this model showed good agreement with the measured values of turbulence. This indicates turbulence could be predicted using these more robust instruments coupled with a machine learning regression model.
Type
Thesis
Description
Series/Report No
Department
Physics (PH)
Organization
Identifiers
NPS Report Number
Sponsors
Office of Naval Research, Arlington, VA 22203-1995
Funding
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
Copyright is reserved by the copyright owner.