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[1] �,HS²,4m�./ÿþÆÄ:.ÉÇ:ÉÇÆÑ�. 2008µ17

[2] Òu,ë.U©/¥ÄåÆ.LH:ìÀÆEâÑ�.2000, 26: 466

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[8] �j',�S,±[÷.ÆÏ�, 2007, 52(15): 1728

[9] Zhu S Y. Prediction of Earth Rotation and Polar Motion. http://adsabs.harvard.edu/abs/1981perp.rept.....Z, 1981: 2

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352 U © Æ ? Ð 29ò352 U © Æ ? Ð 29ò352 U © Æ ? Ð 29ò

Joint Model of Weighted Least-squares and AR in Prediction of Polar

Motion

ZHANG Hao, WANG Qi-jie, ZHU Jian-jun, ZHANG Xiao-hong

(School of Geosciences and Info-Physics, Central South University, Changsha 4100833, China)

Abstract: Earth rotation parameters (ERPs) include length of day and polar motion. Precise trans-

formations between the international celestial and terrestrial reference frames are needed for many

advanced geodetic and astronomical tasks including positioning and navigation on Earth and in

space. To perform this transformation, accurate ERPs are necessary. However, the precise measure-

ments of ERPs by space-geodetic techniques have to be pre-processed before the ERPs are available.

This causes a delay of 15 to 20 hours in case of GPS and of a few days in case of very-long-baseline

interferometry (VLBI) and satellite laser ranging (SLR).Thus it’s necessary to predict the ERPs over

at least a few days. In addition, it might be interesting to look further into the future to estimate the

Earth’s rotation in the next few months. Therefore, this paper deals with short-term predictions for

next 30 days, long-term predictions for 360 days.

Various prediction methods have been developed, such as thejoint model of least-squares and

AR, joint model of least-squares and artificial neural networks(ANN), and so on. These methods

most treat the Chandler Wobble(CW) and Annual Wobble(AW) ofthe polar motion as constants.

However, the CW and AW are of time variant characteristics asa matter of fact. This paper puts

forward a new joint model of weighted least-squares(WLS) and AR, according to the time variant

characteristics of CW and AW. One important issue in building the WLS+AR model is the right

choice of the weight matrix P. According to the statistical properties of the polar motion series,

the rule of weight choice is determined: the fitting value nearer to prediction value is given larger

weight. In accordance with the rule, three kinds of weight function are built and compared in order

to assess the weight function of the weighted least-squares. The more appropriate weight function

for X series and Y series are suggested respectively. Finally the WLS+AR model is compared with

LS+AR model and shown that the new models are effective for improving the accuracy of the PM

prediction. The model is an interesting and new attempt in the PM prediction, and could be seen

as an alternative prediction method. However, in the paper,the theoretical basis of the model is not

analyzed in depth, and which will be further studied in the later research.

Key words: Polar Motion Prediction; Weighted Least-squares; AR Model; Weight Function

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