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Fusion-based Satellite Clock Bias Prediction Considering Characteristics and Fitted Residue

Published online by Cambridge University Press:  05 February 2018

Jicang Lu ,
Chao Zhang ,
Yong Zheng  and
Ruopu Wang
Jicang Lu*
Affiliation:
(Zhengzhou Information Science and Technology Institute, China)
Chao Zhang
Affiliation:
(Zhengzhou Information Science and Technology Institute, China)
Yong Zheng
Affiliation:
(Zhengzhou Information Science and Technology Institute, China)
Ruopu Wang
Affiliation:
(Zhengzhou Information Science and Technology Institute, China)
*
(E-mail: [email protected])
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Abstract

As Satellite Clock Bias (SCB) prediction may be affected by various factors such as periodic items, sampling length, and stochastic items, a fusion-based prediction method is proposed by considering characteristics of SCB and fitted residue. On this basis, an instance algorithm is presented by fusing four typical prediction models. First, we use Empirical Mode Decomposition (EMD) to pre-process and decompose the SCB series into multiple components with various characteristics. Then, we analyse the fitting performance of each model for different components and prediction length, namely short-, mid- and long-term prediction, and select models with the best performance. Next, we analyse fitted residue of the reconstructed SCB, and select the model with the best fitting results. Finally, we fuse the multiple selected models for SCB prediction. The method is tested using Global Positioning System (GPS) precise clock products provided by the International Global Navigation Satellite System Service (IGS). Experimental results show that, compared with single prediction models and existing combination models, the proposed fusion-based prediction method improves accuracy and stability. In particular, the proposed method is more stable and has better performance for mid- and long-term prediction.

Keywords

Satellite clock bias (SCB) predictionEMD (Empirical Mode Decomposition)ResidueMultiple model fusion
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 4 , July 2018 , pp. 955 - 970
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Ai, Q.S., Xu, T.H., Li, J.J. and Xiong, H.W. (2016). The short-term forecast of Beidou satellite clock bias based on wavelet neural network. Proceedings of China Satellite Navigation Conference, 145–154, doi: 10.1007/978-981-10-0934-1_14.CrossRefGoogle Scholar
Batista, P. (2015). Long baseline navigation with clock offset estimation and discrete-time measurements. Control Engineering Practice, 35, 43–53, doi: 10.1016/j.conengprac.2014.10.009.CrossRefGoogle Scholar
Cacciapuoti, L., Dimarcq, N., Santarelli, G., Laurent, P., Lemonde, P., Clairon, A., Berthoud, P., Jornod, A., Reina, F., Feltham, S. and Salomon, C. (2007). Atomic clock ensemble in space: Scientific objectives and mission status. Nuclear Physics B (Proceedings Supplements), 166, 303–306, doi: 10.1016/j.nuclphysbps.2006.12.033.CrossRefGoogle Scholar
Davis, J., Bhattarai, S. and Ziebart, M. (2012). Development of a Kalman filter based GPS satellite clock time-offset prediction algorithm. Proceedings of European Frequency and Time Forum, 152–156, doi: 10.1109/EFTF.2012.6502355.CrossRefGoogle Scholar
Galleani, L. and Tavella, P. (2010). Time and the Kalman filter. IEEE Control Systems Magazine, 30(2), 44–65, doi: 10.1109/MCS.2009.935568.Google Scholar
Galleani, L., Sacerdote, L., Tavella, P. and Zucca, C. (2003). A mathematical model for the atomic clock error. Metrologia, 40(3), S257–S264, doi: 10.1088/0026-1394/40/3/305.CrossRefGoogle Scholar
Griggs, E., Kursinski, E.R. and Akos, D. (2014). An investigation of GNSS atomic clock behavior at short time intervals. GPS Solutions, 18(3), 443–452, doi: 10.1007/s10291-013-0343-7.CrossRefGoogle Scholar
Guo, H.R. (2006). Study on the analysis theories and algorithms of the time and frequency characterization for atomic clocks of navigation satellites. Dissertation, Zhengzhou Information Science and Technology Institute.Google Scholar
Heo, Y.J., Cho, J. and Heo, M.B. (2010). Improving prediction accuracy of GPS satellite clocks with periodic variation behavior. Measurement Science and Technology, 21(7), 073001–1–8, doi: 10.1088/0957-0233/21/7/073001.CrossRefGoogle Scholar
Huang, G.W., Zhang, Q. and Xu, G.C. (2014). Real-time clock offset prediction with an improved model. GPS Solutions, 18(1), 95–104, doi: 10.1007/s10291-013-0313-0.CrossRefGoogle Scholar
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q.A., Yen, N., Tung, C.C. and Liu, H.H. (1998). The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, 454(1971), 903–995, doi: 10.1098/rspa.1998.0193.CrossRefGoogle Scholar
Lei, Y., Hu, Z.P. and Zhao, D.N. (2014). A novel method for navigation satellite clock bias prediction considering stochastic variation behavior. Proceedings of China Satellite Navigation Conference, 369–379, doi: 10.1007/978-3-642-54740-9_32.CrossRefGoogle Scholar
Li, X.X. and Zhao, X.H. (2012). Improving the estimation of uncalibrated fractional phase offsets for PPP ambiguity resolution. Journal of Navigation, 65, 513–529, doi: 10.1017/S0373463312000112.CrossRefGoogle Scholar
Liu, J.Y., Chen, X.H., Liu, Q. and Sun, J.Z. (2013). Prediction of satellite clock errors using LS-SVM optimized by improved artificial Fish Swarm algorithm. Proceedings of IEEE International Conference on Signal Processing, 1–5, doi: 10.1109/ICSPCC.2013.6664130.CrossRefGoogle Scholar
Pratt, J., Axelrad, P., Larson, K.M., Lesage, B., Gerren, R. and DiOrio, N. (2013). Satellite clock bias estimation for iGPS. GPS Solutions, 17(3), 381–389, doi: 10.1007/s10291-012-0286-4.CrossRefGoogle Scholar
Senior, K.L., Ray, J.R. and Beard, R.L. (2008). Characterization of periodic variations in the GPS satellite clocks. GPS Solutions, 12(3), 221–225, doi: 10.1007/s10291-008-0089-9.CrossRefGoogle Scholar
Shi, J.B., Xu, C.Q., Li, Y.H. and Gao, Y. (2015). Impacts of real-time satellite clock errors on GPS precise point positioning-based troposphere zenith delay estimation. Journal of Geodesy, 89(8), 747–756, doi: 10.1007/s00190-015-0811-7.CrossRefGoogle Scholar
Stein, S.R. and Evans, J. (1990). The application of Kalman filters and ARIMA models to the study of time prediction errors of clocks for use in the Defense Communication System (DCS). Proceedings of the 44th Annual Symposium on Frequency Control, 630–635, doi: 10.1109/FREQ.1990.177553.CrossRefGoogle Scholar
Tang, G.F., Xu, X.Q., Cao, J.D., Liu, X.P. and Wang, Q.Y. (2015). Precision analysis for Compass satellite clock prediction based on a universal clock offset model. Science China: Physica, Mechanica & Astronomica, 45(7), 079502–1–6, doi: 10.1360/SSPMA2015-00121.Google Scholar
Wang, J.G. (2010). Research on time comparison based on GPS precise point positioning and atomic clock prediction. Dissertation, University of Chinese Academy of Sciences.Google Scholar
Wang, J.G., Hu, Y.H., He, Z.M., Wu, J.F., Ma, H.J. and Wang, K. (2011). Prediction of clock errors of atomic clocks based on modified linear combination model. Chinese Astronomy and Astrophysics, 35(3), 318–326, doi: 10.1016/j.chinastron.2011.07.009.Google Scholar
Wang, Y.P., Lv, Z.P., Gong, X.C., Zhou, H.T., Wang, N. (2015) Analysing and comparing the prediction performance of several satellite clock bias prediction models. Journal of Geodesy and Geodynamics, 35(3): 373–378, doi: 10.14075/j.jgg.2015.03.003Google Scholar
Wang, Y.P., Lv, Z.P., Qu, Y.Y., Li, L.Y. and Wang, N. (2017). Improving prediction performance of GPS satellite clock bias based on wavelet neural network. GPS Solutions, 21(2), 523–534, doi: 10.1007/s10291-016-0543-z.CrossRefGoogle Scholar
Wang, Y.P., Lv, Z.P., Wang, N., Li, L.Y. and Gong, X.C. (2016). Prediction of navigation satellite clock bias considering clock's stochastic variation behavior with robust least square collocation. Acta Geodaetica et Cartographica Sinica, 45(6), 646–655, doi: 10.11947/j.AGCS.2016.20150569.Google Scholar
Xi, C., Cai, C.L., Li, S.M., Li, X.H., Li, Z.B. and Deng, K.Q. (2014). Long-term clock bias prediction based on an ARMA model. Chinese Astronomy and Astrophysics, 38(3), 342–354, doi: 10.1016/j.chinastron.2014.07.010.Google Scholar
Xu, B., Wang, Y. and Yang, X.H. (2013a). A hybrid model for navigation satellite clock error prediction. Computational Intelligence, 465, 307–316, doi: 10.1007/978-3-642-35638-4_20CrossRefGoogle Scholar
Xu, B., Wang, Y. and Yang, X.H. (2013b). Navigation satellite clock error prediction based on functional network. Neural Processing Letters, 38(2), 305–320, doi: 10.1007/s11063-012-9247-8.CrossRefGoogle Scholar
Xu, X.Q., Zhou, S.S., Shi, S., Hu, X.G. and Zhou, Y.H. (2016). Performance evaluation of the Beidou satellite clock and prediction analysis of satellite clock bias. Proceedings of China Satellite Navigation Conference, 27–35, doi: 10.1007/978-981-10-0940-2_3.CrossRefGoogle Scholar
Yuan, H.B., Wang, Z.M., Dong, S.W., Wu, H.T. and Qu, L.L. (2008). Dynamic grey-autoregressive model of an atomic clock. Metrologia, 45(6), S1–S5, doi: 10.1088/0026-1394/45/6/S01.Google Scholar
Zheng, Z.Y., Lu, X.S. and Chen, Y.Q. (2008). Improved grey model and application in real-time GPS satellite clock bias prediction. Proceedings of the 4th International Conference on Natural Computation, 419–423, doi: 10.1109/ICNC.2008.630.CrossRefGoogle Scholar
Zucca, C. and Tavella, P. (2015). A mathematical model for the atomic clock error in case of jumps. Metrologia, 52(4), 514–521, doi: 10.1088/0026-1394/52/4/514.CrossRefGoogle Scholar