A Gravity Assist Mapping Based on Gaussian Process Regression
نویسندگان
چکیده
Abstract 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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ژورنال
عنوان ژورنال: Journal of The Astronautical Sciences
سال: 2021
ISSN: ['2195-0571', '0021-9142']
DOI: https://doi.org/10.1007/s40295-021-00246-3