Polar motion prediction based on adaptive filtering of variable forgetting factor

Abstract

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.