An adaptive method for selecting the optimal GNSS satellite signal for water vapor tomography

Abstract

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

CLC Number:

P228

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.

Figures/Tables 9

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