GPS/PWV时间序列特征提取方法的研究

Research on a Feature Extraction Method of GPS/PWV Time Series

针对GAMIT/GLOBK软件解算得到的4aGPS/PWV时间序列的特征提取问题,提出基于滤波辅助的部分集成经验模态分解(PEEMD)与Hilbert谱分析相结合的特征提取方法。首先,在PEEMD方法的基础上,结合滤波辅助的PEEMD方法与Hilbert谱分析,建立GPS/PWV时间序列特征提取模型;然后,将所提出的方法应用于TNML测站4a的GPS/PWV长时间序列和7d的GPS/PWV短时间序列分析中,并将滤波辅助的PEEMD结果与传统的小波分解结果进行对比。结果表明,该特征提取方法能准确有效地提取出GPS/PWV时间序列中的周年周期和日周期特征分量,滤波辅助的PEEMD分解结果与小波分解结果一致,且提取的特征分量与原始信号更加吻合。

To extract the features of4-year GPS/PWV time series obtained by GAMIT/GLOBK software,we propose a GPS/PWV feature extraction method based on combination filter-assisted partly ensembled empirical mode decomposition(PEEMD)and Hilbert spectrum.First,filter-assisted PEEMD is combined with Hilbert spectrum to establish the feature extraction model of GPS/PWV.Then,the proposed method is applied to analyze the4-year and7-day GPS/PWV time series at TNML station,and the result of filter-assisted PEEMD is compared with the result of traditional wavelet decomposition.The results show that the proposed feature extraction method can accurately extract the feature components of annual and daily cycles,and that the results of the filter-assisted PEEMD are consistent with those of the wavelet decomposition,and that the extracted feature component are more coincident with the original signal.