基于信号强度的多模多频GNSS-MR雪深反演研究
Multi-mode and multi-frequency GNSS-MR snow depth inversion based on signal strength
- 2023年 页码：1-14
网络出版日期： 2023-08-29
DOI： 10.11834/jrs.20233041
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摘要
近年来，随着全球导航卫星系统（Global navigation satellite systems， GNSS）的发展，兴起了一种基于信噪比（Signal to Noise Ratio，SNR）的GNSS多路径遥感技术（GNSS-Multipath Reflectometry，GNSS-MR）。该技术利用GNSS接收机即可获取反射面信息，在雪深反演中具有信号源丰富、采样率高等优势。但是目前许多GNSS接收机并不记录SNR观测值。为了使这些接收机也具有雪深监测能力，文章提出了一种基于信号强度（Signal Strength Indicator，SSI）的多模多频GNSS-MR雪深反演融合方法。实验研究选取美国阿拉斯加州SG27测站；结果表明：四大全球卫星系统的多频点SSI数据均能反演雪深。经多模多频GNSS-MR雪深反演融合后，SSI反演结果与雪深实测序列间的均方根误差为2.36cm，相关系数为0.98。同时，实验研究也进行了基于SNR数据的多模多频GNSS-MR雪深反演，发现：SSI反演结果和SNR反演结果具有一致性，实验验证了基于SSI的多模多频GNSS-MR雪深反演融合方法的可行性和有效性。
Abstract
In recent years
with the development of Global Navigation Satellite Systems (GNSS)
a GNSS-Multipath Reflectometry (GNSS-MR) technique based on Signal to Noise Ratio (SNR) has been developed. This technology can obtain the information of the reflector by using GNSS receiver
and has the advantages of abundant signal sources and high sampling rate in snow depth inversion. But now many GNSS receiver do not record SNR observations. In order to make these receivers also have the ability of snow depth monitoring
a multi-mode and multi-frequency GNSS-MR snow depth inversion fusion method based on Signal Strength Indicator (SSI) is proposed in this paper. At the same time
aiming at the two main problems existing in GNSS-MR inversion of snow depth
that is
low precision and low time resolution
this method can also be effectively solved. This approach mainly benefits from the strategy of performing a robust estimation. The specific steps are as follows: firstly
using SSI and SNR data of GPS
GLONASS
Galileo and Beidou
and using Lomb-Scargle periodogram (LSP) method in classical snow depth retrieval principle
the snow depth retrieval values of each frequency band are obtained from four constellations. Then a specific time window is established
and the state transition equation set is established in each time window considering the snow surface dynamic change and tropospheric delay. Finally
the snow depth time series is solved by a robust estimation model. In essence
it is a method of optimal valuation for GNSS-MR that is theoretically suitable for different geographical environments. In addition
in order to prove the feasibility and effectiveness of the method
this paper also selected a suitable station for snow depth retrieval experiments. The experimental station is SG27 in Alaska
United States.The results show that the multi-frequency SSI data of four global satellite systems can retrieve snow depth. Before multi-mode and multi-frequency GNSS-MR snow depth inversion fusion
The results of SSI inversion at each frequency band have good correlation with the measured snow depth (except for Beidou frequency band
the other correlation coefficients is greater than 0.92). Considering the standard deviation and root mean square error of the retrieval results of different satellite systems
the retrieval results of GPS satellite system are the best
followed by GLONASS
then Galileo
but the retrieval results of these three satellite systems are similar; The Beidou satellite system has the worst retrieval result. Among the four satellite systems
root mean square error of the frequency band with the best inversion result is 6.34cm. And after multi-mode and multi-frequency GNSS-MR snow depth inversion fusion
the root mean square error between the SSI inversion results and the measured snow depth series is 2.36cm
and the correlation coefficient is 0.98. At the same time
the multi-mode and multi-frequency GNSS-MR snow depth inversion based on SNR data is also carried out in the calculation example
and it is found that:The results of SSI inversion are consistent with those of SNR inversion
and the feasibility and effectiveness of multi-mode and multi-frequency GNSS-MR snow depth inversion fusion based on SSI are verified by experiments.
关键词
GNSS多路径遥感多模多频雪深稳健估计信号强度
Keywords
GNSS-MRmulti-mode and multi-frequencysnow depthrobust estimationsignal strength
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中国气象局国家卫星气象中心
中国科学院大学
中国科学院西北生态环境资源研究院 甘肃省遥感重点实验室 中国科学院黑河遥感试验研究站
新疆大学 资源与环境科学学院
中国科学院新疆生态与地理研究所 荒漠与绿洲生态国家重点实验室
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