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.
Space Debris Risk Prediction Model For LEO Satellites
Advances in intell. Systems Research
International Conference on Deep Learning, Artificial Intelligence and Robotics ; 2024 ; Managalore, India December 06, 2024 - December 08, 2024
Proceedings of the 6th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2024) ; Chapter : 4 ; 31-41
2025-06-25
11 pages
Article/Chapter (Book)
Electronic Resource
English
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