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GNSS线极化天线干涉信号反演土壤湿度算法测试

李杰 杨东凯 洪学宝 王峰

李杰,杨东凯,洪学宝,等. GNSS线极化天线干涉信号反演土壤湿度算法测试[J]. 北京航空航天大学学报,2024,50(3):874-885 doi: 10.13700/j.bh.1001-5965.2022.0282
引用本文: 李杰,杨东凯,洪学宝,等. GNSS线极化天线干涉信号反演土壤湿度算法测试[J]. 北京航空航天大学学报,2024,50(3):874-885 doi: 10.13700/j.bh.1001-5965.2022.0282
LI J,YANG D K,HONG X B,et al. Soil moisture algorithm testing of interference signal inversion with GNSS linearly polarized antenna[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(3):874-885 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0282
Citation: LI J,YANG D K,HONG X B,et al. Soil moisture algorithm testing of interference signal inversion with GNSS linearly polarized antenna[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(3):874-885 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0282

GNSS线极化天线干涉信号反演土壤湿度算法测试

doi: 10.13700/j.bh.1001-5965.2022.0282
基金项目: 国家自然科学基金(42104031)
详细信息
    通讯作者:

    E-mail:wangf.19@163.com

  • 中图分类号: TN 961

Soil moisture algorithm testing of interference signal inversion with GNSS linearly polarized antenna

Funds: National Natural Science Foundation of China (42104031)
More Information
  • 摘要:

    利用全球导航卫星系统干涉信号(GNSS-IR)测量土壤湿度已成为热门的研究课题。搭载低成本线性极化天线的智能手机可以方便快捷采集干涉信号信噪比(SNR)。分别仿真垂直和水平线性极化天线采集的GNSS干涉信号,给出2种极化方式下干涉信号SNR波形和反射率随卫星高度角变化的结果。对于垂直极化分量,电磁波会在入射角65°~85°左右时发生全透射,导致干涉信号振荡效果消失,而水平极化不存在该现象。同时,分别仿真右旋圆极化(RHCP)直射和左旋圆极化(LHCP)反射天线采集的GNSS信号,并计算直反射信号的幅值比。在仿真基础上分别利用不同极化天线进行实验,结果表明:采用线性极化天线采集的GNSS干涉信号振荡效果几乎不受卫星高度角的限制,可以为土壤湿度反演提供更多的有效数据,并且反演得到的土壤湿度与同位数据具有良好的一致性,两者的相关性达到0.95。使用搭载圆极化天线的双通道接收机采集北斗系统卫星数据进行对比,相关性达到0.91。对于不同的设备,智能手机采集的GNSS数据占用空间相对比于双通道接收机降低1%,且反演结果相关性接近,由于干涉信号提取直反射信号需要一定的振荡周期,故反演结果的时间分辨率要低于双通道接收机。

     

  • 图 1  GNSS-IR干涉信号传播几何模型

    Figure 1.  Geometric model of GNSS-IR signal propagation

    图 2  干涉信号 SNR 与反射率

    Figure 2.  Interference signal SNR and reflectivity

    图 3  MEO 卫星直反射信号原始 SNR 和趋势

    Figure 3.  Original SNR and its trend of direct reflection signal from MEO satellite

    图 4  MEO卫星直反射信号幅值比

    Figure 4.  Direct reflected signal amplitude ratio of MEO satellite

    图 5  GEO 卫星直反射信号原始 SNR 和直反射幅值比

    Figure 5.  Original SNR of GEO satellite direct reflection signal, and direct reflection amplitude ratio

    图 6  实验场地

    Figure 6.  Experimental site

    图 7  智能手机反演土壤湿度算法流程图

    Figure 7.  Flowchart of algorithm for soil moisture inversion by smartphone

    图 8  SNR序列

    Figure 8.  SNR sequence

    图 9  LSP谱序列[32]

    Figure 9.  LSP spectral sequence[32]

    图 10  EMD分解后的本征模式函数及对应的LSP谱估计结果

    Figure 10.  Intrinsic mode function EMD, and the corresponding LSP spectral estimation results

    图 11  反演结果与同位数据趋势对比

    Figure 11.  Comparison of inversion results with in-suit data trends

    图 12  实验场地设备架设

    Figure 12.  Experimental site equipment erection

    图 13  反演结果与同位数据散点

    Figure 13.  Scatter plot of inversion results and isotopic data

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出版历程
  • 收稿日期:  2022-04-26
  • 录用日期:  2022-07-30
  • 网络出版日期:  2022-08-08
  • 整期出版日期:  2024-03-27

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