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Research Article

Safe-event pruning in spacecraft conjunction management

Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Te Pūnaha Ātea – Space Institute, The University of Auckland, Auckland 1010, New Zealand
Department of Aerospace Vehicles Design and Control, Institut Supérieur de l’Aéronautique et de l’Espace, Toulouse 31055, France
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Graphical Abstract

Particular attention to spacecraft conjunction management is needed as the number of Earth-orbiting objects grows. Promising data science techniques have been explored in the literature for risk classification. This work utilizes projection, clustering, and optimization techniques to identify low-risk and high-risk regions in the data and determine whether an event can be safely pruned.

Abstract

Spacecraft conjunction management plays a crucial role in the mitigation of space collisions. When a conjunction event occurs, resources and time are spent analyzing, planning, and potentially maneuvering the spacecraft. This work contributes to a subpart of the problem: Confidently identifying events that will not lead to a high collision probability, and therefore do not require further investigation. The method reduces the dimensionality of the data via principal component analysis (PCA) on a subset of features. High-risk regions are then determined by clustering the projected data, and events that do not belong to a high-risk cluster are pruned. A genetic algorithm (GA) is developed to optimize the number of clusters and feature selection of the model. Furthermore, an ensemble learning framework is proposed to combine the suboptimal models for better generalization. The results show that the first set of parameters pruned approximately 50% of the events in the testing set with no false negatives, whereas the second set of parameters pruned 70% of the events and maintained a near-perfect recall. These results could benefit the optimization of operational resources and allow operators to focus better on the events of interest.

Keywords

collision avoidanceensemble learninggenetic algorithm (GA)space situational awareness

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Astrodynamics
Volume 7 Issue 4,
December 2023
Pages 401-413
DOI: 10.1007/s42064-023-0165-5
Cite this article:
Henry S, Armellin R, Gateau T. Safe-event pruning in spacecraft conjunction management. Astrodynamics, 2023, 7(4): 401-413. https://doi.org/10.1007/s42064-023-0165-5

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Received: 10 October 2022
Accepted: 12 May 2023
Published: 15 July 2023
© Tsinghua University Press 2023
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