基于完全自适应噪声集合经验模态分解与小波变换相结合的GPS/BDS-3多路径误差削弱研究

Research on GPS/BDS-3 Multipath Error Reduction Based on Combining Complete Ensemble Empirical Mode Decompositions with Adaptive Noise and Wavelet Transform

多路径误差是GNSS短基线相对定位过程中主要的误差源,已经影响定位的精度。针对经验模态分解(EMD)存在断点效应和模态混叠问题,提出了一种基于完全自适应噪声集合经验模态分解(CEEMDAN)-小波变换(WT)的提取GNSS多路径的方法。通过两天的GPS/BDS-3的实测数据处理分析,实验结果表明,采用CEEMDAN-WT提取多路径相关系数高于小波分析、经验模态分解(EMD),实时削弱多路径误差中使用CEEMDAN-WT比其他两者方法效果更好。

Multipath error is the main error source in the process of GNSS short-baseline relative positioning,which has affected the positioning accuracy.Aiming at the problems of breakpoint effect and modal aliasing in Empirical Mode Decomposition(EMD),a method for extracting GNSS multipath based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)-Wavelet Transform(WT)is proposed.Through the GPS/BDS-3 actual measured data processing and analysis of two days,the experimental results show that using CEEMDAN-WT to extract the multipath correlation coefficient is higher than that of wavelet analysis and Empirical Mode Decomposition(EMD),and using the CEEMDAN-WT to weaken multipath errors in real-time is better than the other two methods.