Abstract:To improve the denoising effect of GNSS height time series, we decompose the simulation signal and height time series of Lhasa station from 2000 to 2020 into several intrinsic mode functions(IMF) by complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) method respectively. We perform wavelet packet multi-threshold decomposition for each IMF component, select different threshold criteria according to the percentage of the energy of different nodes in the total energy of the IMF, reconstruct the noise-reduced node to obtain the noise-reduced IMF component, and then obtain the noise-reduced time sequence. By the indexes of signal-to-noise ratio and root mean square error, we compare and analyze the denoising effect of the proposed method, EMD, CEEMDAN, wavelet denoising and wavelet packet multi-threshold denoising. The results show that the proposed method has the best effect.
YU Hongxu,WEN Hanjiang,LIU Huanling et al. GNSS Height Time Series Denoising Method Based on CEEMDAN and Wavelet Packet Multi-Threshold[J]. jgg, 2022, 42(10): 1005-1009.