A Novel High-Precision Polar Motion Prediction Method Considering Time-Varying Characteristics of Annual and Chandler Components by DE+LS+AR

KONG Qiaoli; WU Puxuan; BO Guoli; WU Yuanwei; LI Xishun; QIAO Haihua; YANG Xuhai; ZHANG Lulu; JING Xiyuan

doi:10.13203/j.whugis20250358

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A Novel High-Precision Polar Motion Prediction Method Considering Time-Varying Characteristics of Annual and Chandler Components by DE+LS+AR

Geodesy and Survey Engineering | 更新时间：2026-11-05

- A Novel High-Precision Polar Motion Prediction Method Considering Time-Varying Characteristics of Annual and Chandler Components by DE+LS+AR
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 5, Pages: 925-934(2026)
- 作者机构：
  
  1.山东科技大学测绘与空间信息学院，山东青岛，266590
  2.中国科学院国家授时中心，陕西西安，710699
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20250358
  CLC： P228
- Received：09 November 2025，
  
  Published：05 May 2026
- 稿件说明：
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孔巧丽,吴普轩,薄国立，等.顾及周期项时变特性的DE+LS+AR高精度极移预报方法[J].武汉大学学报（信息科学版）,2026,51(5):925-934.

KONG Qiaoli,WU Puxuan,BO Guoli,et al.A Novel High-Precision Polar Motion Prediction Method Considering Time-Varying Characteristics of Annual and Chandler Components by DE+LS+AR[J].Geomatics and Information Science of Wuhan University,2026,51(5):925-934.
孔巧丽,吴普轩,薄国立，等.顾及周期项时变特性的DE+LS+AR高精度极移预报方法[J].武汉大学学报（信息科学版）,2026,51(5):925-934. DOI：10.13203/j.whugis20250358

KONG Qiaoli,WU Puxuan,BO Guoli,et al.A Novel High-Precision Polar Motion Prediction Method Considering Time-Varying Characteristics of Annual and Chandler Components by DE+LS+AR[J].Geomatics and Information Science of Wuhan University,2026,51(5):925-934. DOI：10.13203/j.whugis20250358

摘要

极移是地球和天球参考框架相互转换的关键参数，高精度极移预报在飞行器测定轨、实时定位与导航，以及深空探测等领域具有重要意义。为了提高极移预报精度，利用差分进化算法（differential evolution

DE）的全局优化能力，在顾及极移周年摆动和钱德勒摆动周期时变特性的基础上，改进经典最小二乘（least squares

LS）与自回归（autoregressive

AR）组合模型，建立DE+LS+AR极移预报组合模型。实验结果表明，在365 d的极移预报中，

和

分量的预报精度分别优于11.60 m(″)和15.29 m(″)，相对于Bulletin A分别提高37.97%和25.56%，相对于传统LS+AR模型分别提高15.82%和12.98%。实验证明，所提出的预报模型能够有效地捕捉极移数据中周期信号的时变特征，减轻传统LS外推法后期出现的相位漂移，显著提高中长期预报的准确性和鲁棒性。

Abstract

Objectives

Polar motion is a critical parameter for the transformation between terrestrial and celestial reference frames. The high-precision polar motion prediction is essential for the application fields such as satellite orbit determination

navigation

and deep space exploration. Traditional polar motion prediction methods

such as least squares (LS) and autoregressive (AR) model

typically assume that annual wobble (AW) and Chandler wobble (CW) have fixed periods. In reality

however

both AW and CW periods exhibit significant time-varying characteristics

which can lead to phase deviations in the medium- to long-term predictions

and ultimately degrade the prediction accuracy.

Methods

To enhance the medium- to long-term polar motion predictions

this paper proposes a novel approach by integrating the global optimization capability of differential evolution (DE) algorithm into the classical LS+AR

namely DE+LS+AR. A key advancement is the explicit accommodation of time-varying periods of these two primary geophysical oscillations of AW and CW. First

DE algorithm is employed to adaptively determine the optimal time-varying periods of AW and CW based on a 10-year sliding window

maximizing the merits of LS fitting model. Then

the optimized period parameters are incorporated into the LS model

and combined with AR model for fitting and extrapolation prediction of the polar motion series.

Results

The experimental results demonstrate that for 365-day predictions

the accuracy of the proposed DE+LS+AR model is better than 11.60 m(″) and 15.29 m(″) in

and

components

respectively. Compared with Bulletin A

it represents an improvement of 37.97% and 25.56% in

and

components

respectively. And compared with the traditional LS+AR model

it represents an improvement of 15.82% and 12.98%

respectively.

Conclusions

The DE algorithm effectively extracts and optimizes the non-stationary AW and CW periods of polar motion data. These findings confirm that the proposed novel prediction model can effectively capture the time-varying characteristics of the periodic signals in polar motion data. Thereby it can significantly mitigate the phase drift which occurs in the later stages of traditional LS extrapolation

and markedly improve the accuracy of medium- to long-term prediction.

关键词

Keywords

references

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