近年来,随着全球导航卫星系统GNSS(Global Navigation Satellite Systems)的发展,兴起了一种基于信噪比SNR(Signal to Noise Ratio)的GNSS多路径遥感技术GNSS-MR(GNSS-Multipath Reflectometry).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SSI(Signal Strength Indicator)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,4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.
Multi-mode and multi-frequency GNSS-MR snow depth inversion based on signal strength
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 a GNSS receiver and has the advantages of abundant signal sources and high sampling rate in snow depth inversion.However,many GNSS receivers do not record SNR observations.Thus,a multimode and multifrequency GNSS-MR snow depth inversion fusion method based on Signal Strength Indicator(SSI)is proposed in this study to make these receivers capable of snow depth monitoring.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:first,by 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,this study selected a suitable station for snow depth retrieval experiments to prove the feasibility and effectiveness of the method.The experimental station is SG27 in Alaska,United States.Results show that the multifrequency SSI data of four global satellite systems can retrieve snow depth.Before multimode and multifrequency 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.However,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 6.34 cm.After multimode and multifrequency GNSS-MR snow depth inversion fusion,the root mean square error between the SSI inversion results and the measured snow depth series is 2.36 cm,and the correlation coefficient is 0.98.At the same time,the multimode and multifrequency GNSS-MR snow depth inversion based on SNR data is also performed in the calculation example;the results of SSI inversion are consistent with those of SNR inversion,and the feasibility and effectiveness of multimode and multifrequency GNSS-MR snow depth inversion fusion based on SSI are verified by experiments.
GNSS-MRmulti-mode and multi-frequencysnow depthrobust estimationsignal strength
万方数据
维普
