Abstract
Earth Rotation Parameters (ERP) are the necessary parameters to achieve mutual conversion of the celestial reference frame and earth reference frame. It is the requirements of national economic construction and national major strategic defense, and the needs of key frontier disciplines and production applications. In this paper, we take into account the consistency and stability of the Chinese ERP data, and the least-squares combination of Autoregressive (LS+AR) model and the Robust least-squares combination of Autoregressive (RLS+AR) model are proposed to use to predict different span of ERP based on Chinese ERP data, as the cycles and trends have the characteristic of time-varying in the data. The results show that, the RLS+AR model can reduce Influence of crude differential on forecast accuracy effectively, and then achieve the IERS prediction accuracy, and meet the need of national defense in time and space datum height consistency.
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References
Zheng D, Yu N (1996) Earth rotation and it’s relations to geophysical phenomena: the changes of length of the day. Adv Geophys 11(2):81–101 (In Chinese)
McCarthy DD, Luzum BJ (1991) Prediction of earth orientation. J Geodesy 65:18–21
Kosek W, Kalarus M, Niedzielski T (2008) Forecasting of the Earth orientation parameters—comparison of different algorithms. In: Capitaine N (ed) Proceedings of the journèes 2007, systèmes de référence spatio-temporels “The celestial reference frame for the future”. Observatoire de Paris Systèmes de Référence Temps-Espace UMR8630/CNRS, Paris, France, pp 155–158
Kosek W (2010a) Future improvements in EOP prediction. In: Proceedings of the IAG 2009, Geodesy for planet earth, Buenos Aires, Argentina, Aug 31–Sept 4, 2009
Kosek W (2010b) Causes of prediction errors of pole coordinates data. In: Proceedings of the 6th Orlov’s conference. The study of the earth as a planet by methods of geophysics, geodesy and astronomy, MAO NAS of Ukraine, Kiev, Ukraine, 22–24 June 2009, pp 96–103
Niedzielski T, Kosek W (2008) Prediction of UT1-UTC, LOD and AAM χ3 by combination of least-squares and multivariate stochastic methods. J Geodesy 82:83–92
Kosek W, McCarthy DD, Luzum BJ (1998) Possible improvement of Earth orientation forecast using autocovariance prediction procedures. J Geodesy 72:189–199
Liao D, Wang Q, Zhou Y, Liao X, Huang C (2012) Long-term prediction of the earth orientation parameters by the artificial neural network technique. J Geodyn GEOD-1110:6
Schuh H, Ulrich M, Egger D et al (2002) Prediction of Earth orientation parameters by artificial neural networks. J Geodesy 76:247–258
Wang Q (2007). Studies on the prediction of Earth’s variable rotation by Artificial Neural Networks. Shanghai Astronomical observatory, Chinese Academy of Sciences, Shanghai, China (In Chinese)
Akyilmaz O, Kutterer H (2004) Prediction of Earth rotation parameters by fuzzy inference systems. J Geodesy 78:82–93
Sun Z, Xu T (2012) Prediction of earth rotation parameters based on an improved weighted least-squares and AR model. Geodesy Geodyn 3(3):57–64
Sun Z, Xu T (2014) Prediction of polar motion based on combination of least-squares and autoregressive moving average. CSNC2014 2(25):303–311
McCarthy DD, Petit G (2010) IERS Conventions, America
Acknowledgments
This work was supported by Natural Science Foundation of China (41174008) and the Open Foundation of State Key Laboratory of Geodesy and Earth’s Dynamics (SKLGED2013-4-2-EZ) and State Key Laboratory of Astronautic and Dynamics (2014ADL-DW0101).
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Sun, Z., Xu, T., He, B., Ren, G. (2015). Prediction and Analysis of Chinese Earth Rotation Parameters Based on Robust Least-Squares and Autoregressive Model. In: Sun, J., Liu, J., Fan, S., Lu, X. (eds) China Satellite Navigation Conference (CSNC) 2015 Proceedings: Volume II. Lecture Notes in Electrical Engineering, vol 341. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46635-3_40
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DOI: https://doi.org/10.1007/978-3-662-46635-3_40
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