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

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


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.


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



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).


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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Qingsong Ai
    • 1
  • Tianhe Xu
    • 2
    • 3
    Email author
  • 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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