| Citation: | HU Y，YUAN X T，LIU W，et al. GNSS-MR snow depth inversion method based on variational mode decomposition and moving average[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（11）：2890-2897 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0777
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To address the problem of poor separation of the signal-to-noise ratio (SNR) sequence trend items and the significant fluctuation of inversion result in the process of retrieving snow depth using global navigation satellite system multipath reflectometry (GNSS-MR), a snow depth inversion method based on variational mode decomposition (VMD) and moving average (MA) was proposed. The VMD algorithm can effectively separate the trend terms of the SNR sequence through adaptive high-pass filtering, and the MA algorithm can smooth the initial inversion results to reduce random fluctuations. The SNR observations of different frequency bands of GLONASS in the first five months of 2021 from the KIRU station in Sweden were selected to conduct the experiments to investigate the feasibility of the new method. The results show that the correlation coefficient between the inversion results based on the VMD algorithm and the in-situ snow depth of the climate station exceeds 0.95, and the root mean square error (RMSE) is at least about 5 cm, which is nearly 40% less than the traditional method. The inversion accuracy after smoothing using the MA method can be further improved. Considering the differences between GNSS stations and climate station, the GPS SNR inversion results are selected as another reference data source. Consistent experimental conclusions were obtained from different reference data sources, which verified the feasibility and effectiveness of the new method.
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