Estimating Atmospheric Turbulence Within a Short Exposure Frame Selection Algorithm

Abstract

Space is becoming an increasingly crowded domain. As it becomes more congested, the ability to accurately detect and track objects, both natural and man-made, becomes more and more important. Having an accurate space surveillance network allows us to protect our own space assets while also maintaining awareness of foreign space assets and threats to our satellites such as debris and small meteorites. This means that the ability to detect small, dim objects is key to being able to protect our space assets. This thesis will seek to expand on and improve certain existing space object detection techniques and algorithms.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2022
Accession Number
AD1166852

Entities

People

  • Aaron Deluca

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Algorithms
  • Artificial Satellites
  • Atmospheric Motion
  • Background Noise
  • Data Sets
  • Department Of Defense
  • Detection
  • Detectors
  • Engineering
  • Light Sources
  • Literature Surveys
  • Matched Filters
  • Radiation
  • Space Debris
  • Space Force
  • Space Objects
  • Space Surveillance
  • Transfer Functions
  • Turbulence
  • Warning Systems

Readers

  • Astronomy/Astrophysics
  • Military Science and Technology Research and Modernization.
  • Systems Analysis and Design

Technology Areas

  • Space
  • Space - Space Objects