Space Debris Risk Prediction Model For LEO Satellites

DOI

10.2991/978-94-6463-740-3_4How to use a DOI?

Keywords

Low Earth Orbit (LEO); Space debris; Risk prediction model; Orbital Risk Factor (ORF); SVM and Ensemble Classifier; 6G space network

Abstract

As Low Earth Orbit satellites play a vital role in the 6G space network, the risk caused by space debris has become increasingly significant. This paper presents a comprehensive risk prediction model for LEO satellites to enhance the safety and reliability of satellite communication systems. We introduce a novel feature, Orbital Risk Factor (ORF), which Identifies and Detects orbital risk for LEO satellites. This research focuses on the performance evaluation of various machine learning techniques used to address the debris threat to LEO satellites using the data acquired from the European Space Agency (ESA) RCS database. The Synthetic Minority Oversampling Technique (SMOTE) is applied to solve the imbalance classification problem. The experiments show that the Support Vector Machine (SVM) with SMOTE and Ensemble classifier Random Forest, Gradient Boosting, and Extra Tree classifier got the highest accuracy compared to other techniques. The SMOTE based classification increases the accuracy by 10-15%. By applying these advanced classification methods, We identify and categorize debris objects based on their Orbital risk factor for LEO satellites. Our proposed solution involves dynamic risk assessment and predictive analytics to provide real-time alerts and mitigation strategies, ensuring the integrity and efficiency of LEO satellite operations within the 6G network.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Arshee Naz
AU  - Karan Verma
AU  - Geeta Sikka
PY  - 2025
DA  - 2025/06/25
TI  - Space Debris Risk Prediction Model For LEO Satellites
BT  - Proceedings of the 6th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2024)
PB  - Atlantis Press
SP  - 31
EP  - 41
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-740-3_4
DO  - 10.2991/978-94-6463-740-3_4
ID  - Naz2025
ER  -