Abstract
This paper presents the results of the potential application of machine learning techniques, specifically the Random Forest method, to spacecraft operations optimization. The test subject is ESAs INTEGRAL gamma ray observatory with the goal of demonstrating that AI techniques can reliably model the radiation environment of the satellite as it orbits the Earth and passes through the Earths trapped radiation zones in the Van Allen belts. The results clearly demonstrate that machine learning can approximate predictions of complex and dynamic radiation environment within +/-10% provided that an extensive data set is available and is adequately engineered. The consequences of such accurate data-driven predictions are that comprehensive physical models may be, under certain circumstances, an unnecessarily complicated solution to the optimization of scientific operations of Earth orbiting satellites.
Original language | English |
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Title of host publication | 15th International Conference on Space Operations, 2018 |
Publisher | American Institute of Aeronautics and Astronautics Inc, AIAA |
ISBN (Print) | 9781624105623 |
DOIs | |
State | Published - 2018 |
Externally published | Yes |
Event | 15th International Conference on Space Operations, SpaceOps 2018 - Marseille, France Duration: May 28 2018 → Jun 1 2018 |
Publication series
Name | 15th International Conference on Space Operations, 2018 |
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Conference
Conference | 15th International Conference on Space Operations, SpaceOps 2018 |
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Country/Territory | France |
City | Marseille |
Period | 05/28/18 → 06/1/18 |
Funding
The authors would like to acknowledge the funding from the ESOC Intern Programme & DAAD grant with technical support from the Integral FCT (OPS-OAI) and Data Analytics Team for Operations (OPS-OSA)