A Hybrid Model for Navigation Satellite Clock Error Prediction

Part of the Studies in Computational Intelligence book series (SCI, volume 465)


In order to improve navigation satellite clock error prediction accuracy, a hybrid model is proposed in this paper. According to the physics property of atomic clock, the model firstly fits the clock error series by polynomial model. Then it models for polynomial fitting residuals, using functional network. The functional network structure is defined by wavelet de-noising and phase space reconstruction. Finally the GPS satellites are taken for example and four separate predict tests are done, the simulation results show that the proposed method can fit and predict the clock error series effectively, whose predict accuracy is better than those of IGU-P and conventional methods.


Clock error predict Functional network Phase space construction Chaotic Hybrid model 


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  1. 1.
    Delporte, J.: Performance of GPS on board clocks computed by IGS. In: 18th European Frequency and Time Forum, EFTF 2004, Guildford, pp. 201–207 (2004)Google Scholar
  2. 2.
    Zhang, B., Qu, J.K., Yuan, Y.B., et al.: Fitting method for GPS satellites clock errors using wavelet and spectrum analysis. Geomatics and Information Science of Wuhan University 32(8), 715–718 (2007)Google Scholar
  3. 3.
    Cui, X.Q., Jiao, W.H.: Grey system model for the satellite clock error predicting. Geomatics and Information Science of Wuhan University 30(5), 447–450 (2005)Google Scholar
  4. 4.
    Xu, J.Y., Zeng, A.M.: Application of ARIMA(0,2,q) model to prediction of satellite clock error. Journal of Geodesy and Geodynamics 29(5), 116–120 (2009)MathSciNetGoogle Scholar
  5. 5.
    Castillo, E., Cobo, A., Gutiérrez, J.M., et al.: Functional networks with applications: a neural-based paradigm. Kluwer Academic Publishers, Boston (1999)MATHGoogle Scholar
  6. 6.
    Castillo, E., Gutiérrez, J.M.: Nonlinear time series modeling and prediction using functional networks. Extracting information masked by chaos. Physics Letters A 244, 71–84 (1998)CrossRefGoogle Scholar
  7. 7.
    Li, C.G., Liao, X.F., He, S.B., et al.: Functional network method for the identification of nonlinear system. System Engineering and Electronics 23(11), 50–53 (2001)Google Scholar
  8. 8.
    Tomasiello, S.: A functional network to predict fresh and hardened properties of self-compacting concretes. International Journal for Numerical Methods in Biomedical Engineering 27(6), 840–847 (2011)MATHCrossRefGoogle Scholar
  9. 9.
    Martinez, F.G., Waller, P.: GNSS clock prediction and integrity. In: The 22nd European Frequency and Time Forum. IEEE International, Besancon (2009)Google Scholar
  10. 10.
    Ke, X.-Z., Guo, L.-X.: Multi-scale fractal characteristic of atomic clock noise. Chinese Journal of Radio Science 12(4), 396–400 (1997)Google Scholar
  11. 11.
    Castillo, E.: Functional Networks. Neural Processing Letters 7, 151–159 (1998)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Takens, F.: Detecting strange attractors in turbulence. Lecture Notes in Mathematics, vol. 898, pp. 361–381 (1981)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Astronomy & Space ScienceNanjing UniversityNanjingChina
  2. 2.Aerospace System Engineering ShanghaiShanghaiChina
  3. 3.National Time Service CentreXi’anChina

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