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基于VMD和WinLSP相结合的GNSS-R海平面高度估测模型

胡媛 袁鑫泰 刘卫 胡庆松 江志豪 钟李程

胡媛,袁鑫泰,刘卫,等. 基于VMD和WinLSP相结合的GNSS-R海平面高度估测模型[J]. 海洋学报,2022,44(11):170–178 doi: 10.12284/hyxb2022139
引用本文: 胡媛,袁鑫泰,刘卫,等. 基于VMD和WinLSP相结合的GNSS-R海平面高度估测模型[J]. 海洋学报,2022,44(11):170–178 doi: 10.12284/hyxb2022139
Hu Yuan,Yuan Xintai,Liu Wei, et al. GNSS-R sea level height estimation model based on the combination of VMD and WinLSP[J]. Haiyang Xuebao,2022, 44(11):170–178 doi: 10.12284/hyxb2022139
Citation: Hu Yuan,Yuan Xintai,Liu Wei, et al. GNSS-R sea level height estimation model based on the combination of VMD and WinLSP[J]. Haiyang Xuebao,2022, 44(11):170–178 doi: 10.12284/hyxb2022139

基于VMD和WinLSP相结合的GNSS-R海平面高度估测模型

doi: 10.12284/hyxb2022139
基金项目: 上海市自然科学基金(19ZR1422800);国家自然科学基金(52071199);国家重点研发计划(2019YFD0901303)。
详细信息
    作者简介:

    胡媛(1981-),女,江西省萍乡市人,博士,副教授,主要从事海洋遥感和GNSS应用技术研究。E-mail:y-hu@shou.edu.cn

    通讯作者:

    刘卫(1981-),男,上海市人,博士,教授,主要从事GNSS信号处理和遥感应用研究。E-mail:Liu@Satnav.cn

  • 中图分类号: P228

GNSS-R sea level height estimation model based on the combination of VMD and WinLSP

  • 摘要: 全球导航卫星系统反射(Global Navigation Satellite System-Reflectometry,GNSS-R)技术是一种新兴的监测海平面高度变化的技术。本文依据GNSS-R技术中的信噪比分析法的原理,通过分析其分离趋势项和提取振荡频率的过程,建立了新的估测模型以提高反演精度。针对传统模型存在的信号分离不佳的问题,本文提出使用变分模态分解(Variational Mode Decomposition,VMD)算法替换传统的最小二乘拟合法(Least Squares Fitting, LSF)进行趋势项分量的分离。在此基础上,本文引入基于凯塞窗函数改进的LSP(Lomb-Scargle Periodogram)频谱分析法(记为WinLSP)来减弱因频谱泄露带来的反演误差。在瑞典翁萨拉的GTGU站和美国阿拉斯加州的SC02站开展的海平面高度反演实验结果表明,本文建立的估测模型相比于传统模型具有更高的反演精度。基于VMD+WinLSP估测模型得到的GTGU站反演结果的均方根误差(RMSE)、相关系数和反演点数分别为4.70 cm、0.98和5 647。与传统的LSF+LSP估测模型相比,反演精度和GNSS数据利用率分别提高了约29.7%和15.0%。SC02站的RMSE、相关系数和反演点数分别为14.34 cm、0.99和1 785,反演精度和GNSS数据利用率分别提高了约12.3%和9.4%。
  • 图  1  全球导航卫星系统反射(GNSS-R)海平面高度估测模型几何关系图

    Fig.  1  Global navigation satellite systems-reflectometry (GNSS-R) sea level height estimation model geometric relationship diagram

    图  2  信噪比(SNR)数据的本征模态函数(IMF)分量的组成,从上到下,依次是IMF分量(频率从高到低)、残差和原始SNR数据(红线)

    Fig.  2  Composition of the intrinsic mode function (IMF) component of the signal-to-noise ratio (SNR) data, from top to bottom, followed by IMF components (from high frequency to low frequency), residual and original SNR (red line)

    图  3  GTGU站的环境和位置

    Fig.  3  Environment and location of GTGU station

    图  4  趋势项拟合效果对比

    黑色曲线表示原始的信噪比(SNR)数据,蓝、红色曲线分别表示通过最小二乘拟合法(LSF)和变分模态分解算法(VMD)拟合得到的趋势项

    Fig.  4  Comparison of the trend term fitting effect

    The black curve represents the original signal to noise ratio (SNR) data, and the blue and red curves represent the trend terms fitted by the least squares fitting (LSF) method and the variational mode decomposition (VMD) algorithm, respectively

    图  5  GTGU站基于不同趋势项拟合方法的反演结果与验潮站数据的对比情况(a)以及海平面高度误差情况(b)

    Fig.  5  Comparison of inversion results of GTGU Station based on different trend term fitting methods with tide gauge (a) and sea level height error (b)

    图  6  GTGU站基于加窗的频谱分析(WinLSP)反演结果与验潮站数据的对比情况

    Fig.  6  Comparison of inversion results based on lomb-scargle periodogram with window (WinLSP) of GTGU Station with tide gauge

    图  7  SC02站基于不同趋势项拟合方法的反演结果与验潮站数据的对比情况

    Fig.  7  Comparison of the inversion results of SC02 Station based on different trend term fitting methods with the tide gauge

    图  8  SC02站基于加窗的频谱分析的反演结果与验潮站数据的对比情况

    Fig.  8  Comparison of inversion results based on Lomb-Scargle Periodogram with window (WinLSP) of SC02 Station with tide gauge

    表  1  基于变分模态分解(VMD)算法的不同仰角范围的海平面高度反演结果

    Tab.  1  Inversion results of sea level height in different elevation angle ranges based on variational mode decomposition (VMD)

    仰角范围RMSE/cm相关系数反演点数
    5°~15°5.010.982 852
    5°~25°5.180.984 545
    5°~30°5.500.985 268
    下载: 导出CSV

    表  2  基于最小二乘拟合+频谱分析(LSF+LSP)、经验模态分解+频谱分析(EMD+LSP)和变分模态分解+频谱分析(VMD+LSP)的GTGU站海平面高度反演结果的精度对比

    Tab.  2  Accuracy comparison of sea level height inversion results of GTGU Station based on least squares fitting+lomb-scargle periodogram (LSF+LSP), empirical mode decomposition+lomb-scargle periodogram (EMD+LSP) and variational mode decomposition+lomb-scargle periodogram (VMD+LSP)

    方法RMSE/cm相关系数反演点数
    LSF+LSP6.690.964 909
    EMD+LSP5.580.974 641
    VMD+LSP5.500.985 268
    下载: 导出CSV

    表  3  基于最小二乘拟合+加窗的频谱分析(LSF+WinLSP)、经验模态分解+加窗的频谱分析(EMD+WinLSP)和变分模态分解+加窗的频谱分析(VMD+WinLSP)的GTGU站海平面高度反演结果的精度对比

    Tab.  3  Accuracy comparison of sea level height inversion results of GTGU Station based on least squares fitting+lomb-scargle periodogram with window (LSF+WinLSP), empirical mode decomposition+lomb-scargle periodogram with window (EMD+WinLSP) and variational mode decomposition+lomb-scargle periodogram with window (VMD+WinLSP)

    方法RMSE/cm相关系数反演点数
    LSF+WinLSP5.500.975 439
    EMD+WinLSP5.510.975 450
    VMD+WinLSP4.700.985 647
    下载: 导出CSV

    表  4  基于最小二乘拟合+频谱分析(LSF+LSP)、经验模态分解+频谱分析(EMD+LSP)和变分模态分解+频谱分析(VMD+LSP)的SC02站海平面高度反演结果的精度对比

    Tab.  4  Accuracy comparison of sea level height inversion results of SC02 Station based on least squares fitting+lomb-scargle periodogram (LSF+LSP), empirical mode decomposition+lomb-scargle periodogram (EMD+LSP) and variational mode decomposition+lomb-scargle periodogram (VMD+LSP)

    方法RMSE/cm相关系数反演点数
    LSF+LSP16.360.991 632
    EMD+LSP15.830.991 641
    VMD+LSP14.460.991 723
    下载: 导出CSV

    表  5  基于最小二乘拟合+加窗的频谱分析(LSF+WinLSP)、经验模态分解+加窗的频谱分析(EMD+WinLSP)和变分模态分解+加窗的频谱分析(VMD+WinLSP)的SC02站海平面高度反演结果的精度对比

    Tab.  5  Accuracy comparison of sea level height inversion results of SC02 Station based on least squares fitting+lomb-scargle periodogram with window (LSF+WinLSP), empirical mode decomposition+lomb-scargle periodogram with window (EMD+WinLSP) and variational mode decomposition+lomb-scargle periodogram with window (VMD+WinLSP)

    方法RMSE/cm相关系数反演点数
    LSF+WinLSP15.450.991 696
    EMD+WinLSP15.440.991 725
    VMD+WinLSP14.340.991 785
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-11
  • 修回日期:  2022-06-21
  • 网络出版日期:  2022-08-02
  • 刊出日期:  2022-11-03

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