海洋学研究 ›› 2022, Vol. 40 ›› Issue (1): 72-.DOI: 10.3969/j.issn.1001-909X.2022.01.008

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基于LS-SVM的多系统融合GNSS-MR潮位反演

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。


  

  1. 1.南昌大学信息工程学院，江西 南昌 330031； 
    2.自然资源部第二海洋研究所，浙江 杭州 310012
  • 出版日期:2022-03-15 发布日期:2022-03-15
  • 通讯作者: 郭杭(1960-)，男，教授，主要从事多传感器组合导航、GNSS-MR技术、GNSS、INS以及室内定位等方面的研究
  • 作者简介:游高冲(1994-)，男，河北省邯郸市人，主要从事GNSS研究与应用以及GNSS-MR技术应用，E-mali：946066110@qq.com
  • 基金资助:
    国家自然科学基金项目(41764002)

Multi-system fusion GNSS-MR tide level inversion based on LS-SVM

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.#br#

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  • Online:2022-03-15 Published:2022-03-15

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摘要： 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。


关键词: 海平面高度, GNSS-MR, 全球导航卫星系统, 多路径, 机器学习

Abstract: 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.

Key words: sea level, GNSS-MR, GNSS, multipath, machine learning

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