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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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):647–654
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):42–44
Mosavi MR (2011) Wavelet neural network for corrections prediction in single-frequency GPS users. Neural Process Lett 33(2):137–150
Hornik K (1993) Some new results on neural network approximation. Neural Netw 6(09):1069–1072
Pati YC, Krishnaprasad PS (1990) Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations. IEEE Trans Neural Netw 4(1):73–85
Wang YP, Lu ZP (2013) Wavelet neural network algorithm for the prediction of satellite clock bais. J Surveying Mapp 42(3):323–330
Feng ZY (2007) The application and comparative study of wavelet neural network and BP network. Chengdu University of Technology
Jin XF (2012) The application of wavelet neural network in time series. Shanxi Medical University
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 425–430
Wan L, Yang J (2012) The application of wavelet neural network in prediction of short time traffic flow. J Comput Simul 29(9):352–355
Guo FH, Zhang YX. Ying H (2014) Design the prediction of ozone demand based on BP neural network. J Green Sci Technol 7:213–215
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):2813–2819
Zhang S, Wang L, Huang G (2010) New challenges and opportunities in GNSS. Geotech Invest Surveying 38(8):49–53
Yu HL, Hao JM, Liu WP (2014) A method for evaluating the accuracy of satellite clock error. Hydrogr Surveying Charting 02(2):11–13
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Ai, Q., Xu, T., Li, J., Xiong, H. (2016). The Short-Term Forecast of BeiDou Satellite Clock Bias Based on Wavelet Neural Network. In: Sun, J., Liu, J., Fan, S., Wang, F. (eds) China Satellite Navigation Conference (CSNC) 2016 Proceedings: Volume I. Lecture Notes in Electrical Engineering, vol 388. Springer, Singapore. https://doi.org/10.1007/978-981-10-0934-1_14
Download citation
DOI: https://doi.org/10.1007/978-981-10-0934-1_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0933-4
Online ISBN: 978-981-10-0934-1
eBook Packages: EngineeringEngineering (R0)