Academic Journal
顾及周期项时变性的极移预报方法研究.
| Τίτλος: | 顾及周期项时变性的极移预报方法研究. (Chinese) |
|---|---|
| Alternate Title: | Polar Motion Prediction Method Considering Temporal Variability of Periodic Components. (English) |
| Συγγραφείς: | 周雨欣, 魏 娜 |
| Πηγή: | Journal of Geodesy & Geodynamics (1671-5942); Nov2025, Vol. 45 Issue 11, p1185-1191, 7p |
| Θεματικοί όροι: | Prediction algorithms, Least squares, Spectrum analysis instruments, Satellite positioning, Orbital mechanics, Oscillations, Autoregressive models, Statistical reliability |
| Abstract (English): | High-precision polar motion prediction is crucial for applications such as real-time satellite orbit determination and deep space probes navigation. We combine the singular spectrum analysis. (SSA) and weighted least squares (WLS) models, which takes into account the temporal variability of periodic components of polar motion. Combining with the auto-regressive (AR) model, we prove the effectiveness of SSA+WLS+AR model in improving the accuracy of polar motion prediction. We analyze the impact of detrending, the length of basic sequence, and the weighting method on the final prediction accuracy of the SSA + WLS+AR model. The results show that in the X direction, the model combination of "no detrending+50-year basic sequence + inverse weighting" has the highest prediction accuracy, while in the Y direction, the model combination of "detrending +50-year basic sequence+inverse weighting" has the highest prediction accuracy. When the prediction span is from 150 to 360 days, the prediction accuracy of SSA+WLS+AR model is better than that of bulletin A provided by international Earth rotation service (IERS), with maximum improvements of 33% and 26% in the X and Y directions, respectively. The prediction results show that the first-day prediction accuracy of polar motion is significantly better using the C04 20 sequence compared to the C04 14 sequence. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 极移高精度预报对卫星实时定轨、深空探测器导航等应用至关重要。 组合奇异谱分析 (SSA) 和加权 最小二乘 (WLS) 两种顾及极移周期项时变性的预报模型, 并结合自回归 (AR) 模型, 证明 SSA+WLS+AR 模 型对提高极移预报精度的有效性。 在 SSA+WLS+AR 模型中, 分析探讨去均值处理、基础序列长度和加权 方式对最终预报精度的影响。 结果表明, 在 X 方向上, 使用"不进行去均值处理+50a 基础序列+倒数赋权" 的模型组合可以获得精度最高的预报结果; 在 Y 方向上, 使用 "去均值处理+50a 基础序列+倒数赋权" 的模 型组合可以获得精度最高的预报结果。 当预报跨度为 150~360d 时, SSA+WLS+AR 模型的预报精度优 于国际地球自转服务 (IERS) 提供的公报 A 的预报精度, 在 X 和 Y 方向上, 精度提升最大可以分别达到 33% 和 26%。不同序列的预报结果表明, 使用 C04 20 序列时, 极移的第 1 天预报精度显著优于使用 C04 14 序列 时的结果。 [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
καταχωρήστε σχόλιο πρώτοι!