A Gravity Assist Mapping Based on Gaussian Process Regression

Yuxin Liu*, Ron Noomen, Pieter Visser

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
38 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)248-272
Number of pages25
JournalJournal of the Astronautical Sciences
Issue number1
Publication statusPublished - 2021


  • Circular restricted three-body problem
  • Gaussian process regression
  • Gravity assist


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