MACHINE-LEARNING SSA FRAMEWORKS FOR PHOTO-METRIC LIGHT CURVES OF LOW EARTH ORBIT RESIDENT SPACE OBJECTS

Abstract

The modern-day Space Situational Awareness (SSA) context requires the ability to transform and synthesize data from a range of space surveillance sensors, with the goal to extract meaningful and actionable information to enable the effective management of a growing catalogue of resident space objects (RSO). Larger volumes of data can assist in achieving this goal; however increasing the diversity of sensing modalities available to interrogate the nature of the RSO will provide a clear benefit to the process.

Document Details

Document Type
DoD Grant Award
Publication Date
Aug 11, 2021
Source ID
FA23862014034

Entities

People

  • Melrose Brown

Organizations

  • Air Force Office of Scientific Research
  • United States Air Force
  • University of New South Wales

Tags

Readers

  • Aerospace Engineering.
  • Distributed Systems and Data Platform Development
  • Space Exploration and Orbital Mechanics.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Space
  • Space - Space Objects