Spacecraft maneuver strategy optimization for detection avoidance using reinforcement learning

Jason A. Reiter, David B. Spencer, Richard Linares

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Spacecraft maneuvers are planned with operational objectives in mind, usually ranging from making up for orbit perturbations to maneuvering to avoid a possible collision. Though these areas have been researched in depth, performing maneuvers to avoid detection by sensors hasn’t been explored until recently. Reinforcement learning has been shown to be an effective method for optimizing a single detection avoidance maneuver for the purpose of avoiding detection. This work expands on that further by optimizing the maneuver strategy itself that will result in a spacecraft continually avoiding detection through-out a desired time period given a nominal tasking strategy for the opposed sensor.

Original languageEnglish (US)
Title of host publicationAAS/AIAA Astrodynamics Specialist Conference, 2019
EditorsKenneth R. Horneman, Christopher Scott, Brian W. Hansen, Islam I. Hussein
PublisherUnivelt Inc.
Pages3805-3814
Number of pages10
ISBN (Print)9780877036654
StatePublished - 2020
EventAAS/AIAA Astrodynamics Specialist Conference, 2019 - Portland, United States
Duration: Aug 11 2019Aug 15 2019

Publication series

NameAdvances in the Astronautical Sciences
Volume171
ISSN (Print)0065-3438

Conference

ConferenceAAS/AIAA Astrodynamics Specialist Conference, 2019
CountryUnited States
CityPortland
Period8/11/198/15/19

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science

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