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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. En: "8th European Conference on Space Debris", 20–23 April 2021, (Virtual), Darmstadt, Germany.
Título: | Artificial Intelligence for All vs. All Conjunction Screening |
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Autor/es: |
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Tipo de Documento: | Ponencia en Congreso o Jornada (Artículo) |
Título del Evento: | 8th European Conference on Space Debris |
Fechas del Evento: | 20–23 April 2021 |
Lugar del Evento: | (Virtual), Darmstadt, Germany |
Título del Libro: | 8th European Conference on Space Debris |
Fecha: | 2021 |
Materias: | |
Escuela: | E.T.S.I. de Sistemas Informáticos (UPM) |
Departamento: | Sistemas Informáticos |
Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
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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.
ID de Registro: | 67167 |
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Identificador DC: | https://oa.upm.es/67167/ |
Identificador OAI: | oai:oa.upm.es:67167 |
Depositado por: | Emma Stevenson |
Depositado el: | 20 May 2021 09:51 |
Ultima Modificación: | 20 May 2021 09:51 |