Wavelet Extended EMD Noise Reduction Model for Signal Trend Extraction

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 denoising 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), E_NAE and E_Bias with respect to the x/y/z coordinate time series. The results show that the EMD-wavelet model has relative advantage.