Potential Hazardous Asteroids

Efficient orbit and uncertainty propagation

Master thesis (2022)

Authors

J.L. Achterberg Aerospace Engineering

Contributors

R. Noomen Astrodynamics & Space Missions - Aerospace Engineering (supervisor 1)
E. Mooij Astrodynamics & Space Missions - Aerospace Engineering (supervisor 2)
A. Menicucci Space Systems Egineering (supervisor 2)
Faculty
Aerospace Engineering
Copyright: © 2022 Jari Achterberg
More Info
expand_more

Published Date

23-11-2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Aerospace Engineering

Abstract

The number of objects in space is increasing over time, and therefore it is desired to find more efficient propagation models in terms of accuracy and computational speed. This thesis project focuses on the propagation of the state and uncertainties of Potential Hazardous Asteroids, to predict potential Earth impacts. A machine learning technique is proposed to reduce the computational expensiveness of current propagation techniques. The PHA position and the error made by the uncertainties are predicted using a neural network, which uses data from currently known PHAs. Several options are considered regarding the input and output variables and the networks are also tuned. The aim for the best model is to find a reasonable accuracy for the prediction of the position and the uncertainty of newly discovered PHAs.