Abstract
The orbital trajectory of artificial satellites around the Earth requires frequent corrections in response to different perturbation forces. The necessary maneuvers can be designed in simulated space environments by propagating Two Line Elements with orbit propagators such as SGP4, which provides the orbital position information at a given epoch. In this work, a hybrid orbit propagator based on a neural network model is developed. Compared with previous models, the proposed neural network shows generalization capabilities for different space objects, which implies a potential benefit for the accuracy of any classical orbit propagator.
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Notes
- 1.
Two-Line element set format https://celestrak.com/.
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Acknowledgments
This work has been funded by the Spanish State Research Agency and the European Regional Development Fund under Project ESP2016-76585-R (AEI/ ERDF, EU). We have used the Beronia cluster (Universidad de La Rioja), which is supported by FEDER-MINECO grant number UNLR-094E-2C-225.
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Carrillo, H., Segura, E., López, R., Pérez, I., San-Juan, J.F. (2022). Hybrid Orbit Propagator Based on Neural Networks. Multivariate Time Series Forecasting Approach. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_66
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