Neural network based orbit prediction for a geostationary satellite

Date

2001-05-23

Author

Kutay, Ali Türker
Tulunay, Ersin
Tekinalp, Ozan

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An artificial Neural Network (NN) model was developed to estimate the semi-major axis (a), the eccentricity (e) and the inclination (i) of a geostationary satellite orbit. To facilitate a comparison between the NN model developed herewith and a real case, the TORKSAT lB geostationary satellite has been taken as example. A code that numerically solves the parameters of the TORKSAT's orbit, namely METUAEE1, is used to generate the training data for the NN model and to evaluate its performance. A Multi-Layer Perceptron (MLP) type NN model is constructed and it is trained by the Steepest Descent algorithm with one dimensional search. There are two different approaches studied for the estimation of the orbit parameters. In the first one the NN is trained 'on-line' while in the second one 'off-line' training is performed

URI

https://hdl.handle.net/11511/83644

DOI

https://doi.org/10.1016/S1474-6670(17)34317-3

Conference Name

2nd IFAC Workshop on Automatic Systems for Building the Infrastructure in Developing Countries (2001)

Collections

Department of Aerospace Engineering, Conference / Seminar

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Citation Formats

A. T. Kutay, E. Tulunay, and O. Tekinalp, “Neural network based orbit prediction for a geostationary satellite,” Ochrid, Rep of Macedonia, 2001, vol. 34, p. 3, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/83644.