Optimized tracking of low thrust orbit raising maneuvers

Abstract

As a cost savings measure, newly launched commercial satellites are now using Electric Propulsion (EP) thruster technology which uses continuous low thrust maneuvers to raise the orbit (orbit transfer phase). This orbit transfer phase can take months and the orbit geometry will change from Low Earth Orbit (LEO) to Geosynchronous Earth Orbit (GEO) throughout the orbit transfer. The initial orbit is highly eccentric and transitions to circular over the course of the orbit raising, hence, the ability of Electro-Optical (EO) sensors to track it early on is limited by lighting and geometry. As the orbit transfer nears GEO radar tracking becomes more challenging due to longer ranges. Though traditional Radio Frequency (RF) tracking that is used during command and telemetry links can support the orbit determination, it is very expensive over the longer orbit raising period. Furthermore, the transition from LEO to GEO increases the risk of collision as the satellite passes through the various orbit regimes populated by both active and debris objects. This 1-year project proposes to explore novel optimization techniques (e.g. genetic algorithms, adaptive machine learning, covariance-based tasking) to determine the mix of EO, radar and RF tracking over the life-cycle of the orbit raising. The low thrust maneuvers will be accurately characterized using representative thrust profiles. Estimation strategies will be developed. All appropriate constraints will be included, including resource cost, such that the tracking uncertainty is minimized.

Document Details

Document Type
DoD Grant Award
Publication Date
Sep 19, 2018
Source ID
FA95501817006

Entities

People

  • Marco Martorella

Organizations

  • Air Force Office of Scientific Research
  • National Inter-University Consortium for Telecommunications
  • United States Air Force

Tags

Readers

  • Robotics and Automation.
  • Space Exploration and Orbital Mechanics.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Biotechnology
  • Space
  • Space - Orbital Debris
  • Space - Space Objects