@inproceedings{ea7b4c76e0d2485eab69da84c2765f59,
title = "Uncertainty characterization and surrogate modeling for angles only initial orbit determination",
abstract = "Initial orbit determination may be used to initialize object tracking and associate observations with a tracked satellite, but only if uncertainty information exists for the approximated orbit. While classical initial orbit determination algorithms only provide a point solution, uncertainty information may be inferred using deterministic sampling techniques. Along with uncertainty characterization, two statistical learning techniques are tested in their ability to approximate the orbit determination mapping: first, a polynomial approximation built from the statistical moments in the state space and second, Gaussian Process Regression.",
author = "David Schwab and Puneet Singla and Joseph Raquepas",
note = "Funding Information: This research effort is sponsored by the Air Force under MOU FA8750-15-3-6000. The U.S. Government is authorized to reproduce and distribute copies for Governmental purposes notwithstanding any copyright or other restrictive legends. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force or the U.S. Government. 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 = "3599--3616",
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",
}