基于小波自动阈值去噪提高GNSS定位精度

Improve GNSS positioning accuracy based on automatic wavelet threshold denoising

在全球导航卫星系统(Global Navigation Satellite System,GNSS)的变形监测研究中,定位精度受到观测噪声、多路径效应等因素的影响。本文基于小波阈值去噪、Stein无偏风险估计的阈值选择策略提出一种数据驱动的噪声消除策略,旨在利用小波变换提高变形监测的定位精度。首先,将坐标时间序列进行小波分解;其次,基于Stein无偏风险估计获得的阈值进行小波阈值去噪;最后,利用仿真试验和监测站点数据验证所提出方法的有效性,并分别从内符合精度和外符合精度进行精度分析。结果表明,本文所提出的方法可以有效地消除GNSS坐标时间序列中的噪声,提高变形监测的精度。

It is found in the deformation monitoring research of GNSS that the positioning accuracy is af⁃fected by such factors as observation noise and multi-path effects.In this paper,a data-driven noise elimination method is proposed based on wavelet threshold denoising and Stein unbiased risk estimation threshold selection method to improve the positioning accuracy of deformation monitoring by using wavelet transform.Firstly,the coordinate time series are decomposed by wavelet.Secondly,the wavelet thresh⁃old denoising is performed based on the threshold obtained by Stein unbiased risk estimation.Finally,the effectiveness of the proposed method is verified by simulation experiments and monitoring site data,and the accuracy is analyzed from the perspectives of internal and external coincidence accuracy respectively.The results show that the proposed method can effectively eliminate the noise in the GNSS coordinate time series and improve the accuracy of deformation monitoring.