Thu Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Chen, Xiang, Tang, Chengpan, Dai, Wujiao, Hu, Xiaogong, Chen, Liucheng, Zhang, Zhongying, Zhu, Xinhui, and Li, Mingzhe, 2025. Modelling and prediction of atmospheric drag coefficients in LEO satellite orbit determination and prediction with Bi-LSTM approach. Advances in Space Research, 75(3):2874–2888, doi:10.1016/j.asr.2024.10.063.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025AdSpR..75.2874C,
author = {{Chen}, Xiang and {Tang}, Chengpan and {Dai}, Wujiao and {Hu}, Xiaogong and {Chen}, Liucheng and {Zhang}, Zhongying and {Zhu}, Xinhui and {Li}, Mingzhe},
title = "{Modelling and prediction of atmospheric drag coefficients in LEO satellite orbit determination and prediction with Bi-LSTM approach}",
journal = {Advances in Space Research},
keywords = {Low Earth Orbit, Orbit prediction, Atmospheric drag coefficient, Artificial neural networks, Short-arc orbit determination},
year = 2025,
month = feb,
volume = {75},
number = {3},
pages = {2874-2888},
abstract = "{In the precise orbit determination (POD) of Low Earth Orbit (LEO)
satellites with onboard Global Navigation Satellite System
(GNSS) observations, atmospheric drag coefficients (Cd) are
estimated piece-wise to absorb atmosphere density modeling
errors, attitude modeling errors and windward area errors when
the satellite physical metadata is not available. This study
focuses on modeling and prediction of atmospheric drag
coefficient in LEO satellite orbit determination and prediction.
Orbit determination was conducted to determine atmospheric drag
coefficients for eight LEO satellites with the orbital altitudes
from 500 km to 1300 km. The Bidirectional Long Short-Term Memory
(Bi-LSTM) neural network was used to model and predict the
atmospheric drag coefficient estimations. The average Mean
Absolute Percentage Error (MAPE) and average relative error
between the predicted and estimated values of Cd for the eight
satellites, were 0.09 and 0.11, respectively, indicating a
satisfactory prediction performance of Cd. Prediction of the Cd
is applied in orbit prediction and 30-minute short arc orbit
determination (SOD). The results of the orbit prediction show
that the modeling of Cd plays a key role in improving the
accuracy of orbit prediction. The accuracy of the orbit
prediction method based on the Cd prediction is better than that
of the method without Cd prediction, and the average accuracy
improves by 67.5 \% and 73.7 \% for the eight satellites in 2019
and 2023, respectively. The highest accuracy improvement rate is
94.5 \% for GRACE-C satellite in 2019 and 86.6 \% for Swarm-B
satellite in 2023. Among them, the RMS of the average 3D error
of the 3-day orbit prediction of the Swarm-B satellite is the
lowest in both 2019 and 2023, at 2.11 m and 8.79 m,
respectively. The results show that the SOD method with
constrained Cd for eight satellites has different degrees of
accuracy improvement in most arcs relative to the method without
constrained Cd. The average orbital accuracy with constrained Cd
improves by 14.8 \% and 17.1 \% for the eight satellites in 2019
and 2023, respectively, with the highest accuracy improvement of
24.7 \% for GRACE-C satellite in 2019 and 24.2 \% for GRACE-D
satellite in 2023. The average orbit error of GRACE-C satellite
is reduced from 9.23 cm to 5.95 cm, and the average orbit error
of GRACE-D satellite is reduced from 13.45 cm to 8.22 cm.}",
doi = {10.1016/j.asr.2024.10.063},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025AdSpR..75.2874C},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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