针对单系统GNSS-MR潮位监测中反演精度和时间分辨率低的问题，提出了一种基于LS-SVM的多系统融合潮位反演方法。利用香港HKQT站连续30 d的GPS、BDS、Galileo、GLONASS卫星观测数据进行实验，比较了基于滑动窗口最小二乘法、SVR、LS-SVM三种多系统融合方法。结果表明：基于LS-SVM的多系统融合潮位反演与单系统GNSS-MR潮位反演RMSE最小的BDS系统相比，RMSE值减小了55.8%、相关系数提高了4.1%；与单系统潮位反演时间分辨率最高的GLONASS系统相比，时间分辨率提高了59.3%；与基于SVR模型的多系统融合潮位反演相比，RMSE值减小了52.3%，相关系数提高了2.2%；与基于滑动窗口最小二乘法的多系统融合潮位反演相比，RMSE值减小了41.1%，相关系数提高了1.2%。基于LS-SVM的多系统融合潮位反演比滑动窗口最小二乘法、SVR算法具有更优的潮位反演性能。

Aiming at the problem of low inversion accuracy and time resolution in single-system GNSS-MR tide level monitoring, a multi-system fusion tide level inversion method based on LS-SVM was proposed. Using GPS, BDS, Galileo and GLONASS satellite data from Hong Kong's HKQT station for 30 consecutive days, experiments were conducted to compare three multi-system fusion methods based on sliding window least square, SVR, and LS-SVM. The results show that compared with the BDS system with the smallest RMSE for single-system GNSS-MR tide level inversion, the RMSE value of multi-system fusion tide level inversion based on LS-SVM reduced by 55.8% and the correlation coefficient increased by 4.1%. Compared with the GLONASS system with the highest time resolution, the time resolution increased by 59.3%; Compared with the multi-system fusion tide level inversion based on the SVR model, the RMSE value reduced by 52.3%, and the correlation coefficient increased by 2.2%; Compared with the multi-system fusion tide level inversion of the sliding window least square method, the RMSE value reduced by 41.1%, and the correlation coefficient is increased by 1.2%. Multi-system fusion tide level inversion based on LS-SVM has better tide level inversion performance than that of sliding window least square method and SVR algorithm.