Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Li, Bofeng, Wu, Tianhao, and Ge, Haibo, 2025. Deep neural network based precise orbit prediction for low earth orbit (LEO) satellites. Journal of Geodesy, 99(9):75, doi:10.1007/s00190-025-01999-7.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025JGeod..99...75L,
author = {{Li}, Bofeng and {Wu}, Tianhao and {Ge}, Haibo},
title = "{Deep neural network based precise orbit prediction for low earth orbit (LEO) satellites}",
journal = {Journal of Geodesy},
keywords = {Low earth orbit (LEO), Orbit prediction, Deep neural network (DNN), GNSS},
year = 2025,
month = sep,
volume = {99},
number = {9},
eid = {75},
pages = {75},
abstract = "{High-accuracy orbits are the prerequisite for precise applications with
all current Low Earth Orbit (LEO)-borne remote sensing and
future LEO-enhanced GNSS (LeGNSS). Owing to the time latency
mainly caused by the time consumption of computing precise orbit
determination (POD), the predicted orbits of GNSS and LEOs are
inevitably required for real-time applications. However, it is
rather difficult to precisely predict the LEO orbits due to
their perturbation complexity. In this paper, we propose a Deep
Neural Network (DNN) based approach of precise LEO orbit
prediction, where the errors of reduced-dynamic orbit prediction
(RDOP) are further compensated by properly designing DNN-based
Sequence-to-Sequence (Seq2Seq) structures. The GRACE Follow-On
satellites are taken to numerically validate the efficiency of
our method. The results show that with appropriate strategies,
3D RMSEs of 6.9, 14.0 and 22.8 cm can be achieved by RDOP for
half-, 1-, 2-h prediction arc. With the compensation of trained
Seq2Seqs, the prediction accuracies can be significantly
improved by 50{\textendash}80\% for all three directions, where
the RMSEs of 5-min, half-hour, and 1-h are 0.6, 2.9, and 6.0 cm
for 3D, respectively, and 0.4, 1.8, and 3.7 cm for OURE,
respectively. Overall, the predicted orbits with accuracy of 5
cm are achievable for the prediction arc as long as 50 min,
which asserted the great potential of proposed DNN-based
prediction approach in the high-accuracy LEO orbit prediction.}",
doi = {10.1007/s00190-025-01999-7},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025JGeod..99...75L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Mon Oct 13, F. Flechtner