LS+AR极移预报模型的两种修正算法

doi:10.11947/j.AGCS.2018.20180296

测绘学报 ›› 2018, Vol. 47 ›› Issue (S0): 71-77.doi: 10.11947/j.AGCS.2018.20180296

LS+AR极移预报模型的两种修正算法

加松¹, 徐天河^2,3, 杨红雷²

1. 同济大学测绘与地理信息学院, 上海 200092;
2. 山东大学空间科学研究院, 山东威海 246209;
3. 地理信息工程国家重点实验室, 陕西西安 710054

收稿日期:2018-06-24 修回日期:2018-09-21 出版日期:2018-12-31 发布日期:2019-05-18
通讯作者: 徐天河 E-mail:thxugfz@163.com
作者简介:加松(1992-),女,博士生,研究方向为地球自转参数确定及高精度预报。E-mail:jiasong111@163.com
基金资助:
国家重点研发计划（2016YFB0501701）；国家自然科学基金（41874032；41574013）

Two Improved Algorithms for LS+AR Prediction Model of the Polar Motion

JIA Song¹, XU Tianhe^2,3, YANG Honglei²

1. College of Surveying and Geo-information, Tongji University, Shanghai 200092, China;
2. Institute of Space Science, Shandong University, Weihai 246209, China;
3. State Key Laboratory of Geo-information Engineering, Xi'an 710054, China

Received:2018-06-24 Revised:2018-09-21 Online:2018-12-31 Published:2019-05-18
Supported by:
The National Key Research and Development Program of China (No. 2016YFB0501701);The National Natural Science Foundation of China (Nos. 41874032;41574013)

摘要/Abstract

摘要： 极移是表征地球运动的重要参数，其高精度预报值在天文学、大地测量、航海航空、海洋测绘及星际导航等实际应用中具有极其重要的意义。本文在经典组合预报模型最小二乘外推和自回归模型LS+AR的基础上，提出了两种修正算法。一种是利用Kalman滤波对自回归模型进行修正，简称LS+AR+KF模型；另一种是利用最小均方误差自适应滤波（least mean square adaptive filtering，LMSAF）对最小二乘拟合项和外推项进行修正，简称LS+AR+AF模型。计算结果表明，无论LS+AR+KF或LS+AR+AF模型，其预报精度较LS+AR模型都有显著提高，且随着预报跨度的增加其精度提高更为明显；LS+AR+AF模型预报精度要优于LS+AR+KF模型，特别是长期预报结果，其360 d长期预报精度，极移X分量提高26%，Y分量提高23%，极移综合精度提高24%。

关键词: 极移, 预报, Kalman滤波, 最小均方误差, 自适应滤波

Abstract: The polar motion(PM) is the important parameter to represent the earth movement, and the high-precision prediction of PM plays a key role in practical applications of astronomical research, the geodetic survey, navigation, aviation, ocean sounding and interplanetary navigation. Two modified algorithms are proposed to improve the PM prediction accuracy based on combination of least square and autoregressive model (LS+AR). One is to combine Kalman filtering (KF) to improve AR model accuracy, namely LS+AR+KF algorithm. The other is to use least mean square adaptive filtering (LMSAF) to improve the accuracies of LS fitting terms and predicting extrapolations, namely LS+AR+AF algorithm. The results show that LS+AR+KF and LS+AR+AF algorithms can significantly enhance the prediction accuracy of PM especially for long-term perdition, and LS+AR+AF is obviously superior to LS+AR and LS+AR+KF for PM prediction. The accuracy improvement of PM X component, PM Y component and PM can reach about 26%, 23% and 24% respectively, when using LS+AR+AF algorithm.

Key words: polar motion, prediction, Kalman filtering, least mean square, adaptive filtering

中图分类号:

P228

加松, 徐天河, 杨红雷. LS+AR极移预报模型的两种修正算法[J]. 测绘学报, 2018, 47(S0): 71-77.

JIA Song, XU Tianhe, YANG Honglei. Two Improved Algorithms for LS+AR Prediction Model of the Polar Motion[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(S0): 71-77.

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LS+AR极移预报模型的两种修正算法

Two Improved Algorithms for LS+AR Prediction Model of the Polar Motion

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