数据驱动的PPP-RTK多径误差缓解方法及其在变形监测中的应用

doi:10.11947/j.AGCS.2025.20250196

测绘学报 ›› 2025, Vol. 54 ›› Issue (12): 2168-2181.doi: 10.11947/j.AGCS.2025.20250196

数据驱动的PPP-RTK多径误差缓解方法及其在变形监测中的应用

李新瑞¹^,²^,³(), 曲轩宇³(), 张勤¹^,², 舒宝¹^,², 孟岭恩⁴, 许豪¹^,², 张双成¹^,², 黄观文¹^,², 武翰文¹^,², 王利¹^,²

^1.长安大学地质工程与测绘学院，陕西　西安　710054
^2.地理信息工程国家重点实验室，陕西　西安　710054
^3.香港理工大学土地测量及地理资讯学系，香港　999077
^4.西安航天天绘数据技术有限公司，陕西　西安　710100

收稿日期:2025-05-07 修回日期:2025-10-29 出版日期:2026-01-15 发布日期:2026-01-15
通讯作者: 曲轩宇 E-mail:henry-xr.li@polyu.edu.hk;xuany.qu@connect.polyu.hk
作者简介:李新瑞（1998—），男，博士生，研究方向为GNSS变形监测及多路径缓解。　E-mail：henry-xr.li@polyu.edu.hk
基金资助:
国家自然科学基金(42504050; 42127802);国家重点研发计划(2024YFC3012603);陕西省科技创新团队项目(2025JC-YBMS-251);陕西省地学大数据与地质灾害防治创新团队项目(2022);中央高校基本科研业务费专项(300102264905)

A data-driven multipath error mitigation method for PPP-RTK and its application in deformation monitoring

Xinrui LI¹^,²^,³(), Xuanyu QU³(), Qin ZHANG¹^,², Bao SHU¹^,², Lingen MENG⁴, Hao XU¹^,², Shuangcheng ZHANG¹^,², Guanwen HUANG¹^,², Hanwen WU¹^,², Li WANG¹^,²

^1.School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
^2.State Key Laboratory of Geo-Information Engineering, Xi'an 710054, China
^3.Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
^4.Xi'an Aerospace Remote Sensing Data Technology Co., Ltd., Xi'an 710100, China

Received:2025-05-07 Revised:2025-10-29 Online:2026-01-15 Published:2026-01-15
Contact: Xuanyu QU E-mail:henry-xr.li@polyu.edu.hk;xuany.qu@connect.polyu.hk
About author:LI Xinrui (1998—), male, PhD candidate, majors in high-precision GNSS deformation monitoring and multipath mitigation. E-mail: henry-xr.li@polyu.edu.hk
Supported by:
The National Natural Science Foundation of China(42504050; 42127802);The National Key Research and Development Program of China(2024YFC3012603);The Shaanxi Province Science and Technology Innovation Team(2025JC-YBMS-251);The Innovation Team of ShaanXi Provincial Tri-Qin Scholars with Geoscience Big Data and Geohazard Prevention(2022);The Fundamental Research Funds for the Central Universities(300102264905)

摘要/Abstract

摘要：

准确提取建模日的多径误差是实现GNSS变形监测多径误差缓解的重要前提。受精密改正产品精度和参数估计误差的影响，PPP-RTK观测值残差中除多径误差外，往往存在缺乏时空重复性的其他非模型化误差，此类误差在频域内与多径误差混叠，难以通过常规基于频域的滤波策略消除，并在后续处理时被误引入多径改正模型。本文提出一种数据驱动的多径误差缓解方法，首先使用多通道奇异谱分析（MSSA）技术将观测值残差分解为一系列重构分量（RCs），每个重构分量代表一个底层时空信号模态，然后利用不同误差分量固有的时空特性来识别并提取出其中的多径误差并建立多径改正模型。此外，本文在残差提取过程中使用能反映测站真实变形的趋势值替代传统的平均位置参数，从而减少动态监测环境中由位置估计误差引起的系统偏差对残差提取结果的干扰。真实滑坡场景下的PPP-RTK试验结果表明，本文方法在E、N、U方向的定位精度分别为0.89、0.99和2.40 cm，较传统基于小波的恒星日滤波（SF）方法分别提高了约8、9和7个百分比。

关键词: 多径误差, 动态变形监测, PPP-RTK, 恒星日滤波, MHM

Abstract:

Accurately extracting the multipath errors on the modeling day is a crucial prerequisite for effective multipath mitigation in PPP-RTK. However, due to the accuracy of correction products and parameter estimation errors, PPP-RTK observation residuals often contain other unmodeled errors that lack spatiotemporal repeatability. These errors are mixed with multipath errors in the frequency domain, making them difficult to remove using conventional frequency-domain-based filtering methods, and if not properly addressed, may be incorporated into the multipath correction model, thereby degrading positioning accuracy. This paper proposes a data-driven multipath error mitigation method. First, the observation residuals are decomposed into a series of reconstructed components (RCs) using the multi-channel singular spectrum analysis (MSSA) technique, where each RC represents an underlying spatiotemporal signal mode. Then, the inherent spatiotemporal characteristics of different error components are utilized to identify and extract the multipath error and establish a multipath correction model. Moreover, to accommodate the special requirements of dynamic monitoring stations, we replace the traditional mean positional parameter with one that reflects the actual deformation trend of the station during residual extraction. Results from PPP-RTK experiments conducted on a real landslide scenario demonstrate that the proposed method achieves positioning accuracies of 0.89, 0.99 and 2.40 cm in the east, north, and up directions, respectively. Compared to traditional wavelet-based sidereal filtering (SF) methods, the proposed approach improves positioning accuracy by approximately 8, 9 and 7 percentage points, respectively.

Key words: multipath errors, dynamic deformation monitoring, PPP-RTK, sidereal filtering, MHM

中图分类号:

P228

李新瑞, 曲轩宇, 张勤, 舒宝, 孟岭恩, 许豪, 张双成, 黄观文, 武翰文, 王利. 数据驱动的PPP-RTK多径误差缓解方法及其在变形监测中的应用[J]. 测绘学报, 2025, 54(12): 2168-2181.

Xinrui LI, Xuanyu QU, Qin ZHANG, Bao SHU, Lingen MENG, Hao XU, Shuangcheng ZHANG, Guanwen HUANG, Hanwen WU, Li WANG. A data-driven multipath error mitigation method for PPP-RTK and its application in deformation monitoring[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(12): 2168-2181.

图/表 12

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待估参数	表达式
	dt_r+d_IF，_r/c



	M_Pi+ε_Pi
	M_Li+ε_Li

分量	E		N		U
分量	平均/cm	提升/（%）	平均/cm	提升/（%）	平均/cm	提升/（%）
未滤波	1.20	—	1.25	—	2.88	—
WT-SF	0.99	17.5	1.10	12.0	2.64	8.3
MHM	0.93	22.5	1.03	17.6	2.56	11.1
MSSA	0.89	25.8	0.99	20.8	2.40	15.3

数据驱动的PPP-RTK多径误差缓解方法及其在变形监测中的应用

A data-driven multipath error mitigation method for PPP-RTK and its application in deformation monitoring

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