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Publication Type : Journal Article
Publisher : Elsevier BV
Source : Procedia Computer Science
Url : https://doi.org/10.1016/j.procs.2025.04.197
Keywords : Space Debris, K-Means, Satellites, Machine Learning, Skyfield
Campus : Mysuru
School : School of Physical Sciences
Department : Department of Sciences
Year : 2025
Abstract : Space debris presents a growing risk to both satellites and national security, threatening vital sectors like communication, defense, weather monitoring, and scientific research. Addressing this challenge requires advanced and efficient systems for detecting and tracking debris. Traditional methods, such as radar, optical systems, and lasers, have proven effective but carry inherent limitations. In our study, we propose an innovative and cost-effective approach using Two-Line Element (TLE) data and the Skyfield library. TLE data provides orbital parameters of space debris, while the Skyfield library enables real-time tracking and 3D visualization of debris movements, including longitude, latitude, and altitude. By leveraging publicly available resources, our system facilitates comprehensive debris surveillance and collision prevention. Furthermore, incorporating 3D visualization enhances understanding and assists in identifying potential collisions, proving to be a critical tool for space debris management. This research contributes significantly by offering an accessible and efficient solution for space waste monitoring, ensuring the sustainability and security of space operations, while laying the groundwork for future advancements in debris removal strategies.
Cite this Research Publication : Adwitiya Mukhopadhyay, Akshay S, Balasubramanyam K S, Soumik Das, Chiranth M Selar, Vikas M K, Chirag M Selar, Arun Kumar Verma, Space Debris Surveillance: Insights on Trajectory Using Two-Line Element Sets, Procedia Computer Science, Elsevier BV, 2025, https://doi.org/10.1016/j.procs.2025.04.197