随着GNSS反射信号研究的逐渐深入，其在潮位和雪深等地表参数方面的反演理论逐步得到发展。本文利用GNSS反射信号中的多系统多频率SNR数据，基于GNSS-IR技术对潮位和雪深进行反演研究。本文具体研究内容如下：
（1）以MAYG站2021年的潮位反演结果为例，对四大导航系统中不同类型SNR的反演精度进行分析，提出依不同类型SNR整体反演精度排序对各系统反演结果进行单系统多频率数据组合的方法，该方法较单一导航系统反演结果的时间分辨率大幅提升，且可以有效减少反演结果中的冗余数据。
（2）本文提出利用卡尔曼滤波对潮位反演数据进行多系统多频率数据融合，并给出了精确确定状态噪声协方差 和观测噪声协方差 的方法。融合后2021年的潮位反演精度提高了26.35%。为证明该方法的可行性与普适性，利用2017-2020年MAYG站观测数据对该方法进行验证，四年反演精度依次提高了14.78%、13.10%、22.08%和16.52%。在最后，验证了利用2019年反演结果及验潮数据进行2020年数据融合的可行性，融合后反演精度提高了13.55%。
（3）利用SG27测站的观测数据进行雪深反演研究，对四大导航系统中不同类型SNR的反演精度进行分析。引入DBSCAN聚类分析算法对由于地形因素造成的反演误差进行改正，该算法对GPS系统的改正效果最好，反演精度最高提高了34.78%。
（4）在DBSCAN聚类分析的基础上利用卡尔曼滤波对雪深反演时间序列进行误差改正，该方法对四大导航系统各类型信噪比的反演精度均有所提高，尤其对GLONASS和BDS系统的反演精度改善比较明显，误差改善程度较仅使用DBSCAN聚类分析算法最高提升了17.95%。
 

~With the deepening of the research on GNSS reflected signals, its inversion theory for surface parameters such as tide level and snow depth has gradually developed and matured. In this paper, the multi-system multi-frequency SNR data in the GNSS reflected signal is used to invert the tide level and snow depth based on the GNSS-IR technology. The specific research contents of this paper are as follows:
(1) Taking the tidal level inversion results of MAYG station in 2021 as an example, the inversion accuracy of different types of SNRs in the four major navigation systems is analyzed, and a method of combining the inversion results of each system according to the accuracy of different types of SNRs is proposed. Compared with the single navigation system inversion, the time resolution of this method is greatly improved, and the redundant data in the inversion results can be effectively reduced.
(2) In this paper, Kalman filtering is proposed to fuse multi-system and multi-frequency data for tide level inversion data, and a method to accurately determine state noise covariance and observation noise covariance is given. After the fusion, the tidal level inversion accuracy in January 2021 is increased by 26.20%, and the tidal level inversion accuracy for the whole year of 2021 is increased by 26.35%. In order to prove the feasibility and universality of this method, observation data of MAYG station from 2017 to 2020 were used to verify this method, and the inversion accuracy increased by 14.78%, 13.10%, 22.08% and 16.52% respectively in four years. In the absence of current year tide gauge data, this paper verifies the feasibility of using 2019 inversion results and tide survey data for 2020 data fusion, and the inversion accuracy is improved by 13.55% after fusion.
(3) Based on the observation data of SG27 station, the snow depth inversion is studied, and the inversion accuracy of different types of SNR in the four major navigation systems is analyzed. The DBSCAN cluster analysis algorithm was introduced to correct the inversion errors caused by terrain factors. The algorithm had the best correction effect on the GPS system, and the inversion accuracy was improved by up to 34.78%.
(4) On the basis of DBSCAN cluster analysis, Kalman filter is used to correct the error of snow depth inversion time series. This method has improved the inversion accuracy of each type of signal-to-noise ratio of the four major navigation systems, especially the inversion accuracy of GLONASS and BDS systems. The degree of error correction is up to 17.95% higher than that using only the DBSCAN cluster analysis algorithm.
 

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