1 Neiman Yu.M.
2 Sugaipova L.S.

Roskadastr, PLC

The possibilities of using artificial neural networks (ANN) to approximate and extrapolate the orbits of GNSS satellites (global navigation satellite systems) are explored. Numerical experiments are performed using radial basis function networks (RBFN), as they are trained quickly enough and convenient for working with a small amount of data. The results show that RBFN successfully handle both interpolation and short-term satellite orbit prediction (extrapolation) and have notable advantages over traditionally used polynomial methods both in terms of accuracy achieved, and the speed of calculations. In particular, it is possible to use a single neural network to interpolate (extrapolate) all components of the location, while the standard approach requires a separate polynomial for each satellite coordinate
The research was carried out within the framework of the Federal Project “Maintenance, Development and Use of the GLONASS System” of the RF State Program “Space Activities of Russia” for 2021–2030, registration number EGISU No. 1210806000081-5
1.   Pustoshilov A. S., Ushakov Yu. Yu., Tsarev S. P. Dvukhtochechnaya svobodnaya nelineinaya interpolyatsiya koordinat i skorostei navigatsionnykh sputnikov po SP3-dannym. Uspekhi sovremennoi radioelektroniki, 2018, no. 12, pp. 151–155.
2.   Pustoshilov A. S., Tsarev S. P. Vysokotochnoe vosstanovlenie orbit sputnikov GNSS metodom obucheniya po rasshirennym SP3-dannym. Uspekhi sovremennoi radioelektroniki, 2017, no. 12, pp. 48–52.
3.   Feng Y., Zheng Y. (2005) Efficient interpolations to GPS orbits for precise wide area applications. GPS Solutions, no. 9 (4), pp. 273–282. DOI: 10.1007/s10291-005-0133-y.
4.   Gou J., Rösch C., Shehaj E., Chen K., Shahvandi M. K., Soja B., Rothacher M. (2023) Modeling the differences between ultra-rapid and final orbit products of GPS satellites using Machine-Learning Approaches. Remote Sensing, no. 15 (23): 5585, DOI: 10.3390/rs15235585.
5.   Horemuz M., Andersson J. V. (2006) Polynomial interpolation of GPS satellite coordinates. GPS Solutions, no. 10 (1), pp. 67–72. DOI: 10.1007/s10291-005-0018-0.
6.   Preseren P. –., Stopar B. (2009) GPS orbit approximation using radial basis function networks. Computers and Geosciences, no. 35 (7), pp. 1389–1396. DOI: 10.1016/j.cageo.2008.02.038.
7.   Schenewerk M. (2003) A brief of basic GPS orbit interpolation strategies. GPS Solutions, no. 6 (4), pp. 265–267. DOI: 10.1007/s10291-002-0036-0.
8.   Yousif H., El-Rabbany A. (2007) Assessment of several interpolation methods for precise GPS orbit. The Journal of Navigation, no. 60 (3), pp. 443–455. DOI: 10.1017/S0373463307004250.
Neiman Yu.M., 
Sugaipova L.S., 
(2024) GNSS orbit approximation by means of artificial neural networks. Geodesy and cartography = Geodezia i Kartografia, 85(5), pp. 11-23. (In Russian). DOI: 10.22389/0016-7126-2024-1007-5-11-23
Publication History
Received: 08.02.2024
Accepted: 31.05.2024
Published: 20.06.2024


2024 May DOI: