Optimized EWT-NLM Adaptive GNSS Vertical Time Series Noise Reduction Method
Abstract We propose a combinational adaptive noise reduction method combining empirical wavelet transform (EWT) and non-local mean (NLM) filtering with sample entropy (SE) optimization . This method uses SE to determine the low-frequency effective signal limit of all empirical modal components, superimposes the remaining medium and high-frequency components, and performs NLM filtering. Finally, the filtered signal and the effective signal are reconstructed as the final noise reduction signal to filter high-frequency noise. Using simulated data and measured data for experimental research, the results show that the optimized EWT-NLM method is overall better than the EMD and EWT methods. The RMSE decreases by 13.41%/10.63%(measured data/simulated data), 7.13%/5.78%, and the signal-to-noise ratio increases by 22.03%/22.54%, 9.72%/7.42%.
Key words :
GNSS vertical time series
EWT
SE
NLM
signal noise reduction
Cite this article:
LU Tieding,TAO Rui,CHENG Yuanming et al. Optimized EWT-NLM Adaptive GNSS Vertical Time Series Noise Reduction Method[J]. jgg, 2022, 42(5): 451-456.
LU Tieding,TAO Rui,CHENG Yuanming et al. Optimized EWT-NLM Adaptive GNSS Vertical Time Series Noise Reduction Method[J]. jgg, 2022, 42(5): 451-456.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2022/V42/I5/451
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