Evolutionary Prediction of Total Electron Content over Cyprus

  • Alexandros Agapitos
  • Andreas Konstantinidis
  • Haris Haralambous
  • Harris Papadopoulos
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 339)

Abstract

Total Electron Content (TEC) is an ionospheric characteristic used to derive the signal delay imposed by the ionosphere on trans-ionospheric links and subsequently overwhelm its negative impact in accurate position determination. In this paper, an Evolutionary Algorithm (EA), and particularly a Genetic Programming (GP) based model is designed. The proposed model is based on the main factors that influence the variability of the predicted parameter on a diurnal, seasonal and long-term time-scale. Experimental results show that the GP-model, which is based on TEC measurements obtained over a period of 11 years, has produced a good approximation of the modeled parameter and can be implemented as a local model to account for the ionospheric imposed error in positioning. The GP-based approach performs better than the existing Neural Network-based approach in several cases.

Keywords

Evolutionary Algorithms Genetic Programming Global Positioning System Total Electron Content 

References

  1. 1.
    Goodman, J.: HF Communications, Science and Technology, Nostrand Reinhold (1992)Google Scholar
  2. 2.
    Maslin, N.: The HF Communications, a Systems Approach, San Francisco (1987)Google Scholar
  3. 3.
    Klobuchar, J.A.: Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users. IEEE Trans. on AES 23(3), 325–331 (1987)Google Scholar
  4. 4.
    Reeves, C.: Genetic algorithms. In: Handbook of Metaheuristics, pp. 65–82. Kluwer, Dordrecht (2003)Google Scholar
  5. 5.
    Koza, J.R.: Genetic Programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  6. 6.
    Haralambous, H., Vrionides, P., Economou, L., Papadopoulos, H.: A local Total Electron Content Neural Network model over Cyprus. In: Proceedings of the 4th International Symposium on Communications, Control, and Signal Processing (2010)Google Scholar
  7. 7.
    Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley and Sons, Chichester (2002)Google Scholar
  8. 8.
    Goldberg, D.: Genetic Algorithms in Search Optimisation and Machine Learning. Addison-Wesley, Reading (1989)Google Scholar
  9. 9.
    Gagne, C., Schoenauer, M., Parizeau, M., Tomassini, M.: Genetic Programming, Validation Sets, and Parsimony Pressure. In: Proceedings of the 9th European Conference on Genetic Programming, April 10-12. Springer, Heidelberg (2006)Google Scholar

Copyright information

© IFIP 2010

Authors and Affiliations

  • Alexandros Agapitos
    • 1
  • Andreas Konstantinidis
    • 2
  • Haris Haralambous
    • 2
  • Harris Papadopoulos
    • 2
  1. 1.School of Computer Science and InformaticsUniversity College DublinDublinIreland
  2. 2.Computer Science and Engineering departmentFrederick UniversityNicosiaCyprus

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