A Gravity Assist Mapping Based on Gaussian Process Regression

Liu, Y.; Noomen, R.; Visser, P.N.A.M.

doi:10.1007/s40295-021-00246-3

A Gravity Assist Mapping Based on Gaussian Process Regression

Title

A Gravity Assist Mapping Based on Gaussian Process Regression

Author

Liu, Y. (TU Delft Astrodynamics & Space Missions)
Noomen, R. (TU Delft Astrodynamics & Space Missions)
Visser, P.N.A.M. (TU Delft Astrodynamics & Space Missions)

Date

2021

Abstract

We develop a Gravity Assist Mapping to quantify the effects of a flyby in a two-dimensional circular restricted three-body situation based on Gaussian Process Regression (GPR). This work is inspired by the Keplerian Map and Flyby Map. The flyby is allowed to occur anywhere above 300 km altitude at the Earth in the system of Sun-(Earth+Moon)-spacecraft, whereas the Keplerian map is typically restricted to the cases outside the Hill sphere only. The performance of the GPR model and the influence of training samples (number and distribution) on the quality of the prediction of post-flyby orbital states are investigated. The information provided by this training set is used to optimize the hyper-parameters in the GPR model. The trained model can make predictions of the post-flyby state of an object with an arbitrary initial condition and is demonstrated to be efficient and accurate when evaluated against the results of numerical integration. The method can be attractive for space mission design.

Subject

Circular restricted three-body problem
Gaussian process regression
Gravity assist

To reference this document use:

http://resolver.tudelft.nl/uuid:45b5c984-68b9-4799-af32-c182e74bd297

DOI

https://doi.org/10.1007/s40295-021-00246-3

ISSN

0021-9142

Source

Journal of the Astronautical Sciences, 68 (1), 248-272

Part of collection

Institutional Repository