考虑多种权重因子的改进叠加滤波方法

doi:10.13474/j.cnki.11-2246.2023.0207

测绘通报 ›› 2023, Vol. 0 ›› Issue (7): 91-96.doi: 10.13474/j.cnki.11-2246.2023.0207

考虑多种权重因子的改进叠加滤波方法

李晓彤^1,2,3, 李伟^1,2,3,4,5, 颉旭康^1,2,3, 黄宇曈^1,2,3

1. 兰州交通大学测绘与地理信息学院, 甘肃兰州 730070;
2. 地理国情监测技术应用国家地方联合工程研究中心, 甘肃兰州 730070;
3. 甘肃省地理国情监测工程实验室, 甘肃兰州 730070;
4. 特温特大学航天测量与地球科学系, 恩斯赫德 7500;
5. 河西学院土木工程学院, 甘肃张掖 734000

收稿日期:2022-09-02 出版日期:2023-07-25 发布日期:2023-08-08
通讯作者: 李伟。E-mail:geosci.wli@lzjtu.edu.cn
作者简介:李晓彤(1998-),女,硕士生,主要研究方向为GNSS数据处理与分析。E-mail:11200854@stu.lzjtu.edu.cn
基金资助:
中国博士后科学基金(2019M660091XB);甘肃省自然科学基金(20JR10RA271)

Improved stacking filtering method considering multiple weighting factors

LI Xiaotong^1,2,3, LI Wei^1,2,3,4,5, XIE Xukang^1,2,3, HUANG Yutong^1,2,3

1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China;
2. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China;
3. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China;
4. University of Twente, Faculty of Geo-Information Science and Earth Observation, ITC, Enschede 7500, Netherlands;
5. School of Civil Engineering, Hexi University, Zhangye 734000, China

Received:2022-09-02 Online:2023-07-25 Published:2023-08-08

摘要/Abstract

摘要： 针对在提取区域GNSS时间序列共模误差时会存在忽略站间相关性的问题,本文在已有叠加滤波的研究基础上,引入单日解精度、相关系数和距离因子等多种权重因子,提出了考虑多种权重因子的改进叠加滤波方法,并选取山西省测站数据以验证该方法的适用性。结果表明:利用本文的改进叠加滤波方法,测站坐标残差时间序列的均方根在N、E、U 3个分量上可分别平均降低48.53%、39.42%、48.61%,滤波对N、E方向上的速度影响为0.5 mm/a,U方向上为1 mm/a。相较于区域叠加滤波,改进后的方法可使残差时间序列的均方根进一步降低20%～40%,能更加准确地提取共模误差,为区域地壳运动及动力学的分析研究提供精细可靠的数据支持。

Abstract: Aiming at the issue that correlation between stations is often neglected when extracting the common mode error of regional GNSS time series, this paper proposes an improved stacking filtering method which considers multiple weighting factors such as correlation coefficient and distance factor on the basis of existing research on stacking filtering, and analyzes the applicability of the method by selecting data from stations in Shanxi province. The results show that using the improved stacking filtering method in this paper, the root mean square of the coordinate residual time series is reduced by 48.53%, 39.42% and 48.61% on average in the N, E and U components, and the effect of the filtering on the velocity in the N and E directions is 0.5 mm/a and 1 mm/a in the U direction, and Compared with regional superposition filtering, this improved method further reduces the root-mean-square of the residual time series by 20%～40% and can extract the common mode error more accurately, which can provide fine and reliable data support for the study of the mechanism of regional crustal motion and dynamics.

Key words: distance factor, correlation coefficient, GNSS time series, common-mode error, improved stacking filter

中图分类号:

P228

李晓彤, 李伟, 颉旭康, 黄宇曈. 考虑多种权重因子的改进叠加滤波方法[J]. 测绘通报, 2023, 0(7): 91-96.

LI Xiaotong, LI Wei, XIE Xukang, HUANG Yutong. Improved stacking filtering method considering multiple weighting factors[J]. Bulletin of Surveying and Mapping, 2023, 0(7): 91-96.

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考虑多种权重因子的改进叠加滤波方法

Improved stacking filtering method considering multiple weighting factors

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