Comparison of three noise reduction methods for GNSS elevation time series

In order to explore the noise reduction performance of the empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and wavelet analysis, elevation time series data of different lengths from sin IGS stations are taken as examples. Firstly, the outliers and the trend items in the original data are removed to get the sample sequence meeting the experimental requirements. Then, the sample sequence is denoised by three methods and gets the real signal without noise. Finally, calculating the indexes of signal-noise ratio, correlation coefficient and root mean square error of data to compare the three noise reduction methods. The experimental results indicate that: 1) EEMD and wavelet analysis can well denoise when the quality of coordinate time series is poor. 2) Wavelet analysis has the best denoising performance on the Global Navigation Satellite System (GNSS) coordinate time series with time span of 5 a or 10 a; For 20 a time series samples, EEMD and wavelet analysis have similar denoising effects and are better than EMD. 3) The force of wavelet analysis to eliminate colored noise is better.