Reinforcement meta-learning for angle-only intercept guidance of maneuvering targets

Brian Gaudet, Roberto Furfaro, Richard Linares

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

Abstract

We present a novel guidance law that uses observations consisting solely of seeker line of sight angle measurements and their rate of change. The policy is optimized using reinforcement meta-learning and demonstrated in a simulated terminal phase of a mid-course exo-atmospheric interception. Importantly, the guidance law does not require range estimation, making it particularly suitable for passive seekers. The optimized policy maps stabilized seeker line of sight angles and their rate of change directly to commanded thrust for the mis-sile’s divert thrusters. The use of reinforcement meta-learning allows the optimized policy to adapt to target acceleration, and we demonstrate that the policy has superior performance as compared to augmented zero-effort miss guidance with perfect target acceleration knowledge. The optimized policy is computationally efficient and requires minimal memory, and should be compatible with today’s flight processors.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2020 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Pages1-16
Number of pages16
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
Country/TerritoryUnited States
CityOrlando
Period1/6/201/10/20

ASJC Scopus subject areas

  • Aerospace Engineering

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