Artificial Intelligence for All vs. All Conjunction Screening

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.

Descripción

Título: Artificial Intelligence for All vs. All Conjunction Screening
Autor/es:
  • Stevenson, Emma
  • Rodriguez-Fernandez, Victor
  • Urrutxua, Hodei
  • Morand, Vincent
  • Camacho Fernandez, David
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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Resumen

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.

Proyectos asociados

Tipo
Código
Acrónimo
Responsable
Título
Horizonte 2020
813644
Stardust-R
Massimiliano Vasile
Stardust Reloaded

Más información

ID de Registro: 67167
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
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