One hybrid model combining singular spectrum analysis and LS + ARMA for polar motion prediction

详细信息    查看全文

• 作者：Yi Shen^a ; Jinyun Guo^a ; ^b ; ^{jinyunguo1@126.com} ; Xin Liu^a ; Xiaobei Wei^a ; Wudong Li^a
• 关键词：Singular spectrum analysis ; Polar motion ; Prediction ; Least squares ; Autoregressive moving average
• 刊名：Advances in Space Research
• 出版年：2017
• 出版时间：15 January 2017
• 年：2017
• 卷：59
• 期：2
• 页码：513-523
• 全文大小：3383 K
• 卷排序：59

文摘

Accurate real-time polar motion parameters play an important role in satellite navigation and positioning and spacecraft tracking. To meet the needs for real-time and high-accuracy polar motion prediction, a hybrid model that integrated singular spectrum analysis (SSA), least-squares (LS) extrapolation and an autoregressive moving average (ARMA) model was proposed. SSA was applied to separate the trend, the annual and the Chandler components from a given polar motion time series. LS extrapolation models were constructed for the separated trend, annual and Chandler components. An ARMA model was established for a synthetic sequence that contained the remaining SSA component and the residual series of LS fitting. In applying this hybrid model, multiple sets of polar motion predictions with lead times of 360 days were made based on an IERS 08 C04 series. The results showed that the proposed method could effectively predict the polar motion parameters.