Spacecraft stealth through orbit-perturbing maneuvers using reinforcement learning

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

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

1 Scopus citations

Abstract

Spacecraft maneuvers are planned with operational objectives in mind, ranging from making up for atmospheric drag to collision avoidance. 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 maneuver strategy for the purpose of avoiding detection by a sensor with a pre-defined search strategy. This work expands on that further by incorporating the opposed sensor into the learning process as well and results in an optimal strategy for both opponents with respect to one another. It was found that, with an average maneuver magnitude of 19m/s and an average of 3.12 days between maneuvers, a controlled spacecraft is able to successfully avoid being tracked by a single sensor for 31.2% of the observation windows.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2020 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105951
DOIs
StatePublished - 2020
EventAIAA Scitech Forum, 2020 - Orlando, United States
Duration: Jan 6 2020Jan 10 2020

Publication series

NameAIAA Scitech 2020 Forum
Volume1 PartF

Conference

ConferenceAIAA Scitech Forum, 2020
CountryUnited States
CityOrlando
Period1/6/201/10/20

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

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