Compensation of Target Image Aberrations for Military Systems using Machine Learning

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.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2022
Accession Number
AD1184881

Entities

People

  • John C. Gale

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Adaptive Optics
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Atmospheric Motion
  • Computer Programs
  • Computer Vision
  • Computers
  • Convolutional Neural Networks
  • Deep Learning
  • Engineering
  • Guided Projectiles
  • High Energy
  • Laser Beams
  • Machine Learning
  • Neural Networks
  • Schools
  • Training
  • United States
  • United States Naval Academy
  • Unmanned Aerial Vehicles

Fields of Study

  • Physics

Readers

  • Image Processing and Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Directed Energy