Policy gradient approach for dynamic sensor tasking applied to space situational awareness

Richard Linares, Roberto Furfaro

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

Abstract

This paper studies the sensors tasking and management problem for optical Space Object (SO) tracking. The tasking problem is formulated as Markov Decision Process (MDP) and solved using Reinforcement Learning (RL). This RL problem is solved using actor-critic policy gradient approach. This approach is used to find the optimal policy for tasking optical sensors to estimate SO orbits. The reward function is based reducing the uncertainty for the overall catalog to a given upper bound. The reward is negative as long as a SO exist that is about the desired catalog uncertainty. This work tests this approach in simulation and good performance is found using the actor-critic policy gradient approach.

Original languageEnglish (US)
Title of host publicationSpaceflight Mechanics 2017
PublisherUnivelt Inc.
Pages1247-1257
Number of pages11
Volume160
ISBN (Print)9780877036371
StatePublished - 2017
Event27th AAS/AIAA Space Flight Mechanics Meeting, 2017 - San Antonio, United States
Duration: Feb 5 2017Feb 9 2017

Other

Other27th AAS/AIAA Space Flight Mechanics Meeting, 2017
CountryUnited States
CitySan Antonio
Period2/5/172/9/17

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science

Fingerprint Dive into the research topics of 'Policy gradient approach for dynamic sensor tasking applied to space situational awareness'. Together they form a unique fingerprint.

  • Cite this

    Linares, R., & Furfaro, R. (2017). Policy gradient approach for dynamic sensor tasking applied to space situational awareness. In Spaceflight Mechanics 2017 (Vol. 160, pp. 1247-1257). Univelt Inc..