用卡尔曼波器进行极地运动最小方形+多变量自行回归混合方法的超短期预测
Zhirong Tan1,2, Fei Ye1, Liangchun Hua1
1Bei Dou High-Precision Satellite Navigation and Location Service Hunan Engineering Research Center, Hunan Institue of Geomatics Sciences and Technology, Shaoshanzhong Road No. 693, Changsha 410007, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究引入了一种新的混合方法,以高精度预测极运动 (PM). LS+MAR+Kalman模型显著改善了对卫星导航至关重要的超短期预测.
科学领域:
- 地质物理学和空间地质测量
- 时间序列分析时间序列分析
- 预测建模预测建模
背景情况:
- 极地运动 (PM) 的超短时间预测对于卫星和航天器的实时导航至关重要.
- 像LS+AR和LS+AR+Kalman这样的现有方法在实现预测准确度方面存在局限性.
- 需要探索混合方法来提高PM预测性能.
研究的目的:
- 提出和评估新的LS+MAR+Kalman混合方法,用于超短时间的PM预测.
- 通过使用已建立的数据集,分析这种新混合模型的预测性能.
- 为了证明提高PM预测准确性的潜力,关键空间应用.
主要方法:
- 实现最小平方 (LS) 方法与多变量自回归 (MAR) 建模相结合.
- 整合卡尔曼波器来改进LS+MAR模型的预测.
- 在IERS EOP 14 C04系列上测试LS+MAR+Kalman混合方法,用于1-10天的预测.
主要成果:
- LS+MAR+Kalman混合方法证明了有效的超短期预测能力,特别是对于PMy.
- 在10天的预测中,PMy预测准确度达到了12.69%的显著改善.
- 在所有测试期间,超短期预测的平均改善率为7.64%.
结论:
- LS+MAR+Kalman混合方法在超短期极地运动预测方面取得了重大进展.
- 这种增强的准确性对于优化卫星和航天器导航系统的性能至关重要.
- 对混合建模方法的进一步研究是有必要的,以改善地球定向参数预测.
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