A Gravity Assist Mapping Based on Gaussian Process Regression
作者： Ron Noomen , Pieter Visser , Yuxin Liu
DOI: 10.1007/S40295-021-00246-3
关键词:
摘要: We develop a Gravity Assist Mapping to quantify the effects of flyby in two-dimensional circular restricted three-body situation based on Gaussian Process Regression (GPR). This work is inspired by Keplerian Map and Flyby Map. The allowed occur anywhere above 300 km altitude at Earth system Sun-(Earth+Moon)-spacecraft, whereas map typically cases outside Hill sphere only. performance GPR model influence training samples (number distribution) quality prediction post-flyby orbital states are investigated. information provided this set used optimize hyper-parameters model. trained can make predictions state an object with arbitrary initial condition demonstrated be efficient accurate when evaluated against results numerical integration. method attractive for space mission design.
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