Artificial Intelligence for All vs. All Conjunction Screening

Stevenson, Emma and Rodriguez-Fernandez, Victor and Urrutxua, Hodei and 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.

Description

Title: Artificial Intelligence for All vs. All Conjunction Screening
Author/s:
  • Stevenson, Emma
  • Rodriguez-Fernandez, Victor
  • Urrutxua, Hodei
  • Morand, Vincent
  • Camacho Fernandez, David
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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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Horizon 2020813644Stardust-RMassimiliano VasileStardust Reloaded

More information

Item ID: 67167
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
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