GPS multipath effect snow depth estimation by Gaussian process regression-assisted

doi:10.13474/j.cnki.11-2246.2022.0207

Abstract

Abstract: This article studies the global positioning system- interferometric reflectometry technology. Based on the GPS monitoring data of P101 station provided by the Plate Boundary Observatory in the United States, it utilizes the obvious feature of multipath effect when the altitude angle of GPS satellite is lower than certain angle, constructs Gaussian process regression-assisted(GPR-assisted)GPS interference reflection snow depth estimation model and monitors the snow depth around GPS stations. The results show that the accuracy of the snow depth estimation value output by the GPR-assisted GPS interference reflection snow depth estimation model is improved to varying degrees compared with the traditional single-satellite inversion result, and the change trend of GPR snow depth estimation value is closer to the change of actual snow depth, which provides a new idea for surface snow depth inversion.

Key words: GPR, GPS, signal to noise, interferometric reflectometry, snow depth retrieval

CLC Number:

P228

JIA Xiuli. GPS multipath effect snow depth estimation by Gaussian process regression-assisted[J]. Bulletin of Surveying and Mapping, 2022, 0(7): 78-82.

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