Polar Motion Prediction Based on Adaptive Filtering of Variable Forgetting Factor

被引：0
|
作者：
Jia, Song [1 ]
Xu, Tianhe [2 ,3 ]
Yang, Honglei [1 ]
机构：
[1] Changan Univ, Sch Geol Engn & Surveying, Xian 710054, Shanxi, Peoples R China
[2] Shandong Univ, Inst Space Sci, Weihai 246209, Shandong, Peoples R China
[3] State Key Lab Geoinformat Engn, Xian 710054, Shanxi, Peoples R China
来源：
2017 FORUM ON COOPERATIVE POSITIONING AND SERVICE (CPGPS) | 2017年
关键词：
Polar Motion; Prediction; variable forgetting factor; Adaptive Filtering;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The Polar Motion (PM) is the important parameter of Earth Rotation Parameters (ERP), and the high-precision prediction of PM plays a key role in the applications of autonomous orbit determination, the geodetic survey, navigation and aviation. In this paper, a modified algorithm is proposed to improve the PM prediction accuracy based on combination of Least Square and Autoregressive Model (LS+AR). An adaptive filtering of variable forgetting factor is developed to amend the LS fitting terms and predict extrapolations, which is named LS+AR+AF algorithm. The numerical results show that LS+AR+AF algorithm can significantly enhance the prediction accuracy of PM, especially for the long-term perdition. The accuracy improvement of 360-day prediction for PM X component, PM Y component and total PM can reach 30.66%, 28.19% and 29.59% respectively, when using LS+AR+AF algorithm.
引用
收藏
页码：245 / 250
页数：6
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