一种GNSS卫星信号自适应优选的水汽层析方法

摘要/Abstract

摘要：

现有GNSS水汽层析研究主要聚焦于如何提升卫星观测数据利用率，但在卫星信号数据优选方面研究较少，导致穿过同一组网格集的层析观测方程线性近似且方程系数矩阵列向量元素多数为零，水汽层析模型病态严重。针对该现状，本文提出一种GNSS卫星信号自适应优选的水汽层析方法，解决层析模型设计矩阵零元素较多和层析模型病态的难题。该方法基于网格覆盖率最大原则确定层析区域水平网格划分，并发展联合卫星高度角与方位角阈值的卫星信号自适应优选方法，克服水汽层析模型观测方程线性近似的难题。本文选取香港2013年5月2日—2013年5月7日共6 d 12个GNSS测站及1个无线电探空站数据为例进行试验。与现有方法相比，本文方法能在降低卫星信号利用率的同时保证网格覆盖率，克服相似卫星信号造成层析模型设计矩阵病态的现状。以无线电探空数据为真值，发现本文方法反演水汽密度廓线的平均RMS、MAE和Bias分别为1.03、0.80和0.13 g/m³，优于传统方法的1.25、0.97和0.10 g/m³，其RMS改善率为20.78%；此外，本文方法在模型解算效率方面也优于传统方法，其模型计算效率平均提升9.51%。

关键词: GNSS, 水汽层析, 数据优选, 模型计算效率

Abstract:

Existing research on GNSS water vapor tomography primarily focuses on improving the utilization of satellite observation data, but there is limited study on the optimization of satellite signal data. This leads to the linear approximation of the tomography observation equations for the same set of grid groups, with most elements of the coefficient matrix column vectors being zero, resulting in severe ill-conditioning of the water vapor tomography model. To address this issue, this paper proposes an adaptive optimization method for GNSS satellite signals in water vapor tomography, aiming to solve the problems of numerous zero elements in the design matrix and the ill-conditioning of the tomography model. This method determines the horizontal grid division of the tomography region based on the principle of maximum grid coverage and develops an adaptive optimization approach for satellite signals by combining elevation and azimuth angle thresholds, thereby overcoming the challenge of linear approximation in the observation equations of the water vapor tomography model. Experimental data from 12 GNSS stations and 1 radiosonde station in Hong Kong from May 2 to 7, 2013, were selected for experiment. Compared to existing methods, the proposed approach ensures grid coverage while reducing satellite signal utilization, addressing the issue of ill-conditioning in the design matrix caused by similar satellite signals. Using radiosonde data as the truth values, the proposed method demonstrates superior performance, with the average RMS, MAE, and Bias of the retrieved water vapor density profiles being 1.03, 0.80, and 0.13 g/m³, respectively, outperforming the traditional methods' values of 1.25, 0.97, and 0.10 g/m³. The RMS improvement rate is 20.78%. Additionally, the proposed optimal selection method also shows superior model solving efficiency compared to traditional methods, with an average improvement of 9.51% in computation efficiency.

Key words: GNSS, water vapor tomography, data optimization, model computation efficiency

中图分类号:

P228

赵庆志, 蒋朵朵, 姚宜斌, 马智, 马永杰, 李浩杰, 薛瑞瑞. 一种GNSS卫星信号自适应优选的水汽层析方法[J]. 测绘学报, 2026, 55(1): 25-35.

Qingzhi ZHAO, Duoduo JIANG, Yibin YAO, Zhi MA, Yongjie MA, Haojie LI, Ruirui XUE. An adaptive method for selecting the optimal GNSS satellite signal for water vapor tomography[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(1): 25-35.

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