A novel hybrid EMD-based drift denoising method for a dynamically tuned gyroscope (DTG)

Abstract

Empirical mode decomposition (EMD), a new self-adaptive signal processing method, has been recently developed for nonlinear and non-stationary time series analysis. In this paper, EMD method is described and employed for signal denoising. Aiming at the problems of intrinsic mode function (IMF) criterion in the EMD method, neural network (NN) prediction model and wavelet packet transform (WPT) technology are simultaneously introduced into the EMD method to improve the border effect and to enhance the ability of signal denoising, and thus a type of hybrid EMD-based model is proposed and applied in drift denoising for a dynamically tuned gyroscope (DTG). Experimental results on real drift data from the long-term measurement system of a certain DTG indicate that the proposed hybrid model is feasible and more effective in drift denoising compared with the wavelet and single EMD denoising methods.