Signal Extraction Based on an Improved EMD Method

Abstract

The main purpose of this paper is to propose an improved empirical mode decomposition (EMD) approach based on principal component analysis (PCA). EMD is a time-frequency analytical method used to deal with non-linear and non-stable signals. But it generally fails to separate IMFs from primordial signal consisting of several adjacent frequencies,especially when noise is strong.In this papar the PCA is introduced to solve this problem.First,PCA is used to pro-process sample signal to get principal components. Then several signals are reconstructed by principal components.Reconstructing signals can separate adjacent frequencies and depress noise.With the EMD method again,right characteristic information is obtained from intrinsic mode functions (IMFs). Simulated signal is analyzed by the proposed method, as shown by example. Compared to the EMD method, this improved method is proved to be superior to the traditional EMD method in extracting the characteristics of signal,which consisting adjacent frequencies and interfered by strong noise.