@inproceedings{de09409b8b864ed7beaf3ed04e5b3cfe,
title = "Spacecraft maneuver strategy optimization for detection avoidance using reinforcement learning",
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{\textquoteright}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.",
author = "Reiter, {Jason A.} and Spencer, {David B.} and Richard Linares",
note = "Publisher Copyright: {\textcopyright} 2020, Univelt Inc. All rights reserved.; AAS/AIAA Astrodynamics Specialist Conference, 2019 ; Conference date: 11-08-2019 Through 15-08-2019",
year = "2020",
language = "English (US)",
isbn = "9780877036654",
series = "Advances in the Astronautical Sciences",
publisher = "Univelt Inc.",
pages = "3805--3814",
editor = "Horneman, {Kenneth R.} and Christopher Scott and Hansen, {Brian W.} and Hussein, {Islam I.}",
booktitle = "AAS/AIAA Astrodynamics Specialist Conference, 2019",
address = "United States",
}