The Optimization Select Method of Spacecraft Initial Orbit Based on K-means Clustering Algorithm

DOI

10.2991/jiaet-18.2018.32How to use a DOI?

Keywords

k-means clustering; initial orbit; semi-long axis; weight

Abstract

In the rocket active segment mission, the spacecraft and rocket separation orbit after the initial orbit number is an important basis to determine the success of rocket launchers. At present, there are many data sources that can be used to determine the number of initial orbits. However, it is preferable to rely mainly on manual decisions, resulting in long time-consuming and easily disturbed by the site environment, and the decision-making efficiency and accuracy are not high. A method based on K-means clustering algorithm for spacecraft initial trajectory optimization is proposed, which is automatically classified by machine learning and automatically optimized according to a predetermined strategy to improve decision efficiency and accuracy.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dingxin Yang
AU  - Cong Gao
AU  - Peng Du
PY  - 2018/03
DA  - 2018/03
TI  - The Optimization Select Method of Spacecraft Initial Orbit Based on K-means Clustering Algorithm
BT  - Proceedings of the 2018 Joint International Advanced Engineering and Technology Research Conference (JIAET 2018)
PB  - Atlantis Press
SP  - 185
EP  - 189
SN  - 2352-5401
UR  - https://doi.org/10.2991/jiaet-18.2018.32
DO  - 10.2991/jiaet-18.2018.32
ID  - Yang2018/03
ER  -