联合EMD-HD和小波分解的GNSS坐标时间序列降噪分析EI北大核心CSCD

针对经验模态分解(empirical mode decomposition,EMD)在GNSS坐标时间序列的降噪过程中存在筛选准则的选取和模态混叠效应等问题,本文引入Hausdorff距离(Hausdorff distance,HD)筛选准则并结合小波分解(wavelet decomposition,WD),提出EMD-HD&WD算法。通过对我国大陆构造环境监测网络149个GNSS测站的垂向坐标时间序列降噪处理,分别利用复合指标T值、测站的速度不确定度和闪烁噪声振幅验证算法的可靠性和普适性。结果显示:HD优于现有的筛选准则;EMD-HD&WD算法对测站的速度不确定度和闪烁噪声振幅的平均改正率均为88.4%。分析表明,本文算法能够有效识别和剔除噪声并且改善EMD的模态混叠效应,提高GNSS垂向坐标时间序列的模型精度。

Denoising analysis of GNSS coordinate time series by combining EMD-HD and wavelet decomposition

This study proposes the EMD-HD&WD algorithm to solve the limitations of screening rules and mode mixing in the denoising analysis of the GNSS coordinate time series when using empirical mode decomposition (EMD). The improved algorithm introduces the Hausdorff distance (HD) as a screening criterion for EMD and combines wavelet decomposition (WD). The reliability and universality of EMD-HD&WD algorithm were verified by 149 GNSS vertical time series in the crustal movement observation network of China. The results show that the HD is better than the existing screening criteria; the average correction rates of the EMD-HD&WD for the velocity uncertainty and the amplitude of flicker noise of GNSS stations are 88.4%. The algorithm can effectively identify the observational noise, reduce the mode aliasing of EMD, and improve the model accuracy of GNSS vertical coordinate time series.