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Stevenson, Emma, Rodriguez-Fernandez, Victor, Urrutxua, Hodei, Morand, Vincent and Camacho Fernandez, David (2021). Artificial Intelligence for All vs. All Conjunction Screening. In: "8th European Conference on Space Debris", 20–23 April 2021, (Virtual), Darmstadt, Germany.
Title: | Artificial Intelligence for All vs. All Conjunction Screening |
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Author/s: |
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Item Type: | Presentation at Congress or Conference (Article) |
Event Title: | 8th European Conference on Space Debris |
Event Dates: | 20–23 April 2021 |
Event Location: | (Virtual), Darmstadt, Germany |
Title of Book: | 8th European Conference on Space Debris |
Date: | 2021 |
Subjects: | |
Faculty: | E.T.S.I. de Sistemas Informáticos (UPM) |
Department: | Sistemas Informáticos |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
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This paper presents a proof of concept for the application of artificial intelligence (AI) to the problem of efficient, catalogue-wide conjunction screening. Framed as a machine learning classification task, an ensemble of tabular models were trained and deployed on a realistic all vs. all dataset, generated using the CNES BAS3E space surveillance simulation framework, and consisting of 170 million object pairs over a 7-day screening period. The approach was found to outperform classical filters such as the apogee-perigee filter and the Minimum Orbital Intersection Distance (MOID) in terms of screening capability, with the number of missed detections of the approach controlled by the operator. It was also found to be computationally efficient, thus demonstrating the capability of AI algorithms to cope and aid with the scales required for current and future operational all vs. all scenarios.
Item ID: | 67167 |
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DC Identifier: | https://oa.upm.es/67167/ |
OAI Identifier: | oai:oa.upm.es:67167 |
Deposited by: | Emma Stevenson |
Deposited on: | 20 May 2021 09:51 |
Last Modified: | 20 May 2021 09:51 |