An Efficient Noise Elimination Method for Non-stationary and Non-linear Signals by Averaging Decomposed Components

Abstract

In this paper, a moving-average method of smoothing noise based on complex exponential decomposition is applied to eliminate noise of a non-stationary signal and a non-linear signal produced by Bouc–Wen model, which are added to white Gaussian noise to simulate the noise in measured signal. The method uses a sliding window cutting the entire non-stationary and/or non-linear signal into small segments and considers that the small segments are stable and linear. The segments are decomposed into a series of components via complex exponential decomposition, and the high-energy components are reserved to reconstruct de-noised signal. Then, due to the overlap of the reconstructed segments, the average value at the same time point of reconstruction signal is regarded as the de-noised data. A non-stationary signal and a non-linear signal are selected to investigate the performance of the proposed method, the results show that the proposed method has better de-noising efficiency compared with the wavelet shrinkage method and the Savitzky–Golay filter method based on EMD (EMD-SG) for dealing with the signals with SNR of 10 dB, 15 dB, and 20 dB, and de-noised signal using the proposed method has the highest signal-to-noise ratio (SNR) and the least root mean square error (RMSE).