PREDICTION OF THE EM SIGNAL DELAY IN THE IONOSPHERE USING NEURAL MODEL

Zoran Stanković, Nebojša Dončov

DOI Number
10.2298/FUEE1902287S
First page
287
Last page
302

Abstract


Neural model capable to accurately and efficiently predict a propagation delay of electromagnetic signal in the ionosphere is proposed in this paper. The model performs this prediction for a given geographic location in Europe between 40° (N) and 70° (N) latitude and 10° (W) and 30° (E) longitude, according to the following parameters: particular day in a year, time during the day and frequency of a signal carrier. Architecture of the model consists of four multilayer perceptron (MLP) networks with the task to estimate, for the known values of the previously mentioned input parameters of the model, the approximate value of free ions concentration in the atmosphere along the signal propagation path above the geographic location of the receiver. Based on the estimated ions concentration and taking into account the considered frequency of the signal carrier, the model calculates the time delay of signal propagation in the ionosphere. The developed neural model is applicable on the whole territory of Republic of Serbia, for all four weather seasons in the period of low solar activity. The results of using the proposed model for the prediction of time delay of the GPS (Global Positioning System) signal in the area of city of Niš are provided in the paper.


Keywords

neural networks, neural model, ionosphere, total electron content, signal delay estimation, global positioning system

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References


M. Dragović, Antene i prostiranje radio talasa, Beopres, Beograd, 1996.

T. Pratt, C. W. Bostian, J. E.Allnutt, Satellite Communications, John Wiley and Sons, 2003. god.

S. Basu, J. Buchau, F.J. Rich, E.J. Weber, E.C. Field, J.L. Heckscher, P.A. Kossey, E.A. Lewis, B.S. Dandekar, L.F. McNamara, E.W. Cliver, G.H. Millman, J. Aarons, S. Basu, J.A. Klobuchar, S. Basu, M.F. Mendillo, Ionospheric Radio Wave Propagation, Chapter 10, pp. (10-1)–(10-111), 1985.

J. A. Klobuchar and J. Aarons, Numerical Models of Total Electron Content Over Europe and the Mediterranean and Multi-station Scintillation Comparisons, INTERNATIONAL AGARD Agardograph, 1973.

K. F. Tapping, "The 10.7 cm solar radio flux (F10.7)", Space Weather, vol. 11, pp. 394–406, 2013.

Ionosphere Monitoring and Prediction Center (IMPC), Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), German Aerospace Center, URL: http://impc.dlr.de/.

R. Orus, M. Hernandez-Pajares, J.M. Juan and J. Sanz, "Improvement of global ionospheric VTEC maps by using kriging interpolation technique", Journal of Atmospheric and Solar-Terrestrial Physics, vol. 67, no. 16, pp. 1598–1609, 2005.

B.K. Choi, W.K. Lee, S.K. Cho, J.U. Park and P.H. Park, "Global GPS Ionospheric Modelling Using Spherical Harmonic Expansion Approach", Journal of Astronomy and Space Sciences, vol. 27, no. 7, pp. 359–366, 2010.

S. Haykin, Neural Networks, New York, IEEE, 1994.

Q. J. Zhang, K. C. Gupta, Neural Networks for RF and Microwave Design, Artech House, 2000.

C. Christodoulou, M. Georgiopoulos, Applications of Neural Networks in Electromagnetics, Artech House, 2001.

R.F. Leandro, M.C. Santos, "A neural network approach for regional vertical total electron content modelling", J. Studia Geophys. Geod., vol. 51, issue 2, pp. 279–292, 2007.

M.R.G. Razin, B. Voosoghi and A. Mohammadzadeh, "Efficiency of artificial neural networks in map of total electron content over Iran", Acta Geod. Geophys., issue 3, pp. 1–15, 2015.

M.R.G. Razin, B. Voosoghi, "Wavelet neural networks using particle swarm optimization training in modeling regional ionospheric total electron content", J. Atmos. Sol. Terr. Phys., vol. 149, pp. 21–30, 2016.

M. J. Homam, "Prediction of Total Electron Content of the Ionosphere using Neural Network", Jurnal Teknologi (Sciences & Engineering), vol. 78, no. 5–8, pp. 53–57, 2016.

Z. Stanković, I. Milovanović, J. Jovanović, N. Dončov, B. Milovanović, “Estimation of the EM Wave Propagation Delay in the Ionosphere using Artificial Neural Networks”, Presented at the YUINFO 2017 Conference, March 12-15, Kopaonik, Serbia, 2017, only a short abstract printed.

Z. Stankovic, I. Milovanovic, N. Doncov, M. Sarevska and B. Milovanovic, "Estimation of the Carrier Phase Advance of the EM signal in the Ionosphere Using Neural Model", In Proceedings of the 52nd International Scientific Conference on Information, Communication and Energy Systems and Technologies, Niš, Serbia, June 28 - 30, 2017, pp. 211–215.

R. Song, X. Zhang, C. Zhou, J. Liu and J. He, "Predicting TEC in China based on the neural networks optimized by genetic algorithm", Advances in Space Research, vol. 62, no. 4, pp. 745–759, 2018.


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