COMPENSATION OF TARGET IMAGE ABERRATIONS FOR MILITARY SYSTEMS USING MACHINE LEARNING
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Authors
Gale, John C.
Subjects
machine learning
AI
deep learning
target imaging
high energy laser
HEL
High Energy Laser Beam Control Research Testbed
HBCRT
AI
deep learning
target imaging
high energy laser
HEL
High Energy Laser Beam Control Research Testbed
HBCRT
Advisors
Agrawal, Brij N.
Kim, Jae Jun
Date of Issue
2022-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
High energy laser (HEL) systems are susceptible to atmospheric turbulence when focusing on targets down range. Current HEL systems use wavefront sensors and complex adaptive optics systems to compensate for these aberrations. The primary objective of this thesis is to investigate target image aberration compensation techniques using machine learning algorithms, eliminating the need for complex wavefront sensing hardware. Target imagery will be obtained from the High Energy Laser Beam Control Research Testbed (HBCRT) and imagery aberrations will be simulated to provide necessary datasets for training and validation of the image aberration compensation methods. The performance of these techniques will be evaluated for military imaging applications.
Type
Thesis
Description
Series/Report No
Department
Mechanical and Aerospace Engineering (MAE)
Organization
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NPS Report Number
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Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.