Abstract
In this paper, the structure of multi-scale decomposition and reconstruction of empirical mode decomposition (EMD) theory is defined. Through the combination of wavelet and EMD theory, a new EMD-Wavelet dynamic deformation data de-noising model is proposed. The model is presented to reduce noise of coordinate time series. Firstly, the non-linear series are decomposed into stationary IMFs and residual components. Secondly, the selected high frequency IMFs are de-noised with the wavelet model and finally, the EMD reconstruction gives the extracted time series. Compared with the denosing models based on Wavelet, Kalman and EMD, the EMD-Wavelet model has relatively higher Signal-to-Noise Ratio(SNR) than other models and the lowest Root Mean-Square Error (RMSE) �» NAE E and Bias E with respect to the x/y/z coordinate time series. The results show that the EMD-Wavelet model has relative advantage.
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