TY - GEN
T1 - Satellite State Prediction and Maneuver Detection Analysis Using NCDEs
AU - Lee, Kangjun
AU - Woo, Simon S.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Satellite Orbit Propagator (SOP) is of prime importance in the prevention of collision and completion of the assigned task of the satellites. In the past, orbit prediction and propagation have relied on physics-based mathematical models. However, as the number of satellites and their data increases, it is crucial to explore the data-driven orbit propagation based on advanced machine learning methods. In this work, we propose a novel deep learning-based framework to forecast future satellite orbit states. The proposed framework employs a model based on Neural Controlled Differential Equations (NCDEs) to train orbit prediction models, and our approach captures features from past satellite state values at both fixed and dynamic time intervals. The experimental results on Korea Aerospace Research Institute (KARI)’s KOMPSAT-3 and 5 datasets demonstrate that the proposed framework outperforms the other eight data-driven baseline forecasting models.
AB - Satellite Orbit Propagator (SOP) is of prime importance in the prevention of collision and completion of the assigned task of the satellites. In the past, orbit prediction and propagation have relied on physics-based mathematical models. However, as the number of satellites and their data increases, it is crucial to explore the data-driven orbit propagation based on advanced machine learning methods. In this work, we propose a novel deep learning-based framework to forecast future satellite orbit states. The proposed framework employs a model based on Neural Controlled Differential Equations (NCDEs) to train orbit prediction models, and our approach captures features from past satellite state values at both fixed and dynamic time intervals. The experimental results on Korea Aerospace Research Institute (KARI)’s KOMPSAT-3 and 5 datasets demonstrate that the proposed framework outperforms the other eight data-driven baseline forecasting models.
KW - Orbit Forecasting
KW - Satellite Orbit Maneuver Detection
KW - Satellite Orbit Propagation
KW - Time Series Analysis
UR - https://www.scopus.com/pages/publications/85213301230
U2 - 10.1007/978-3-031-78189-6_15
DO - 10.1007/978-3-031-78189-6_15
M3 - Conference contribution
AN - SCOPUS:85213301230
SN - 9783031781889
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 225
EP - 241
BT - Pattern Recognition - 27th International Conference, ICPR 2024, Proceedings
A2 - Antonacopoulos, Apostolos
A2 - Chaudhuri, Subhasis
A2 - Chellappa, Rama
A2 - Liu, Cheng-Lin
A2 - Bhattacharya, Saumik
A2 - Pal, Umapada
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th International Conference on Pattern Recognition, ICPR 2024
Y2 - 1 December 2024 through 5 December 2024
ER -