Abstract:Aiming at the problem that GPS elevation time series is affected by various types of noise, which makes it difficult to extract useful information, we propose a threshold denoising method based on ensemble empirical mode decomposition (EEMD) and multi-scale permutation entropy (MPE). The method uses EEMD as the core algorithm. EEMD can decompose the original signal into a series of intrinsic modal function (IMF), use MPE as an indicator to classify it into noise IMF, hybrid IMF and information IMF, and then use threshold function to process the mixed IMF to achieve secondary noise reduction. Finally, the noise-reduced data and information IMF are reconstructed to obtain noise reduction results. By analyzing the simulation signals and examples, the results show that compared with the correlation coefficient method and the MPE method, the noise reduction evaluation indexes RMSE, SNR, and dnSNR are all optimal, indicating that the new method has the best noise reduction effect. It further demonstrates that the noise reduction results obtained by the new method can better reflect the nonlinear variation characteristics of the time series itself, and can provide a reliable basis for GPS elevation time series analysis.