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潮位是保证沿海安全、监测海洋气候、维持高程基准的重要参数。近年来基于地基Global Navigation Satellite Systems (GNSS)反射信号的遥感方法被证实可以用于潮位监测。相较于传统的潮位测量方法，GNSS-multipath reflectometry (GNSS-MR)技术有成本低、连续跟踪、全天候等优势；但是目前技术的精度不高、时间分辨率较低。通过获取更多GNSS卫星系统的观测数据可以提高潮位监测结果的时间分辨率，本文利用GPS、GLONASS、Galileo和BeiDou的观测数据，采用基于IGGIII模型的稳健回归方法对四系统的潮位反演数据进行融合研究。测站选取BRST站和HKQT站，这两个测站均可接收四系统数据；实验结果表明，利用多模多频GNSS-MR进行潮位反演，二个测站的反演精度分别优于13cm和 8cm，相比于单系统单频精度有40%-70%的提升，而且能够大大提高时间分辨率。
Sea level is an important parameter to ensure coastal safety, monitor marine climate and maintain elevation datum. In recent years, the remote sensing method using ground-based GNSS reflection signal can be used for sea level monitoring. Compared with the traditional sea level measurement method, GNSS multipath reflectometry (GNSS-MR) technology has the advantages of low cost, continuous tracking and can observe all-weather and all day. However, GNSS-MR technology is limited by two problems: low accuracy and low time resolution. The time resolution of retrievals can be improved by acquiring more observation data from more satellite systems. In this paper, a robust regression strategy based on the IGGIII scheme is proposed to address the two limitations. This method uses the SNR data of GPS, GLONASS, Galileo and Beidou, the Lomb–Scargle periodogram (LSP) method in the classical tide level inversion principle is used to obtain the sea level estimates of each frequency band from quad-constellation, then a specific time window is established, and the state transition equation set is established in each time window considering the sea surface dynamic change and tropospheric delay. Finally，the sea level time series is solved by a robust estimation model. In order to prove the feasibility and effectiveness of this method, BRST station in France and HKQT station in Hong Kong were chosen to validate the performance of the proposed method. The root-mean-square errors (RMSEs) between sea level combined retrievals of multi-GNSS signals and the tide gauge records are calculated. The RMSE of BRST station is 12.43cm, which is about 40% - 60% higher than the results of single signal of each system. The RMSE of HKQT station is 7.09cm, which is about 72% higher than the results of the four systems. Both BRST station and HKQT station can formulate a 10-min sea level time series, which greatly improves the time resolution of sea level retrievals compared with single signal retrievals. Comparing the inversion results of the two stations, it can be concluded that using robust regression strategy based on the IGGIII scheme can lead to a clear increase in precision and achieve a higher temporal sampling because of the more frequent GNSS retrievals and a better retrieval combination strategy. The estimated value of sea level has good agreement with the data of tide gauge records, and can clearly describe the fluctuation of sea level. In essence, it is a method of quality control and optimal valuation for GNSS-MR that is theoretically suitable for different geographical environments.