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The Short-Term Forecast of BeiDou Satellite Clock Bias Based on Wavelet Neural Network

  • Qingsong Ai
  • Tianhe Xu
  • Jiajing Li
  • Hongwei Xiong
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 388)

Abstract

According to nonlinear and nonstationary characteristics of BeiDou satellite clock bias time series, this paper proposed a method using the wavelet neural network (WNN) based on the first-order difference of adjacent epoch to predict the satellite clock bias. Experimental data with sampling interval of 15 min rapid and ultra-rapid satellite clock bias provided by Wuhan University is used to test the validation of the method. The results show that the forecast precision of 6 h for BeiDou satellite can reach 1–2 ns, and the 24 h can reach 2–4.6 ns using the proposed method. The test results also show that the accuracy and stability of the model prediction can be improved greatly using the proposed method compared to the traditional gray model and quadratic polynomial model.

Keywords

Satellite clock bias First-order difference of adjacent epoch Wavelet neural network 

Notes

Acknowledgments

Thanks to BDS satellite clock bias products provided by the analysis center of Wuhan University. This study is supported by the foundation of natural science of china (Grant No. 41174008 and 41574013) and open foundation of state key laboratory of aerospace dynamics (Grant No. 2014ADL-DW0101).

References

  1. 1.
    Cui XQ, Jiao WH (2005) The application of grey model in satellite clock bias prediction. J Wuhan Univ 30(5):447–450 (Information Science Edition)Google Scholar
  2. 2.
    Allan DW (1987) Time and frequency (time-domain) characterization, estimation, and prediction of precision clocks and oscillators. IEEE Trans Ultrason Ferroelectr Freq Control 34(6):647–654Google Scholar
  3. 3.
    Zhu LF, Wu XP, Li C (2007) Defect analysis of grey model in the prediction of satellite clock bais. J Astronaut Metrol Meas 27(04):42–44Google Scholar
  4. 4.
    Mosavi MR (2011) Wavelet neural network for corrections prediction in single-frequency GPS users. Neural Process Lett 33(2):137–150Google Scholar
  5. 5.
    Hornik K (1993) Some new results on neural network approximation. Neural Netw 6(09):1069–1072Google Scholar
  6. 6.
    Pati YC, Krishnaprasad PS (1990) Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations. IEEE Trans Neural Netw 4(1):73–85Google Scholar
  7. 7.
    Wang YP, Lu ZP (2013) Wavelet neural network algorithm for the prediction of satellite clock bais. J Surveying Mapp 42(3):323–330Google Scholar
  8. 8.
    Feng ZY (2007) The application and comparative study of wavelet neural network and BP network. Chengdu University of TechnologyGoogle Scholar
  9. 9.
    Jin XF (2012) The application of wavelet neural network in time series. Shanxi Medical UniversityGoogle Scholar
  10. 10.
    Chen Z, Feng, TJ, Meng QC (1999) The application of wavelet neural network in time series prediction and system modeling based on multiresolution learning. In: IEEE conference on systems, man, and cybermetics, vol 1, pp 425–430Google Scholar
  11. 11.
    Wan L, Yang J (2012) The application of wavelet neural network in prediction of short time traffic flow. J Comput Simul 29(9):352–355Google Scholar
  12. 12.
    Guo FH, Zhang YX. Ying H (2014) Design the prediction of ozone demand based on BP neural network. J Green Sci Technol 7:213–215Google Scholar
  13. 13.
    Yang Y, Li J, Xu J et al (2011) Contribution of the compass satellite navigation system to global PNT users. Chin Sci Bull 56(26):2813–2819Google Scholar
  14. 14.
    Zhang S, Wang L, Huang G (2010) New challenges and opportunities in GNSS. Geotech Invest Surveying 38(8):49–53Google Scholar
  15. 15.
    Yu HL, Hao JM, Liu WP (2014) A method for evaluating the accuracy of satellite clock error. Hydrogr Surveying Charting 02(2):11–13Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Qingsong Ai
    • 1
  • Tianhe Xu
    • 2
    • 3
  • Jiajing Li
    • 1
  • Hongwei Xiong
    • 1
    • 4
  1. 1.School of Geology Engineering and SurveyingChang’an UniversityXi’anChina
  2. 2.State Key Laboratory of Geo-Information EngineeringXi’anChina
  3. 3.Xi’an Research Institute of Surveying and MappingXi’anChina
  4. 4.School of information engineeringChina University of GeoscienceBeijingChina

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