AI-Driven Space Debris Detection and Trajectory Prediction System

DOI

10.2991/978-94-6239-707-1_16How to use a DOI?

Keywords

orbital mechanics; deep learning; space debris; object detection; trajectory prediction; collision avoidance; and Space Situational Awareness (SSA)

Abstract

The proliferation of space debris in Earth’s orbit poses an escalating challenge to satellite operations, space missions, and the longterm sustainability of orbital environments. Traditional debris monitoring systems, which predominantly utilize ground-based radar and optical telescopes, encounter limitations in scalability and are not optimally designed for autonomous and real-time operations. This study introduces an AI-driven system for space debris detection and trajectory prediction, integrating deep learning–based optical detection using YOLOv8 with Two-Line Element (TLE)-based orbital propagation and collision risk assessment. The SGP4 model was employed to predict the three-dimensional orbital trajectories of catalogued objects, while optical imagery facilitated debris detection and conjunction analysis. These combined capabilities enhance decision-making for collision avoidance and satellite-maneuver planning. Compared to conventional monitoring approaches, the proposed system offers enhanced automation, reduced operational costs, and expedited response times. However, its performance is contingent on optical visibility conditions and is constrained when addressing newly detected debris that lacks available TLE data. Future research will focus on overcoming these challenges to further improve the robustness and applicability of the system.

Copyright

© 2026 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  - Diksha Rade
AU  - Sanskruti Salve
AU  - Devesh Peandbhaje
AU  - Priyanshu Jaiswal
AU  - Prajval Said
AU  - Ganesh Ubale
PY  - 2026
DA  - 2026/06/18
TI  - AI-Driven Space Debris Detection and Trajectory Prediction System
BT  - Proceedings of the International Conference on Recent Advances in Intelligent and Sustainable Technologies (RAIST 2026)
PB  - Atlantis Press
SP  - 181
EP  - 194
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-707-1_16
DO  - 10.2991/978-94-6239-707-1_16
ID  - Rade2026
ER  -