Research Of Time-Frequency Method Based On EEMD And Simulation System Design

Hilbert-Huang Transform (HHT) is a new time-frequency analysis method which is especially developed for analyzing the non-linear and non-stationary signal. The key part of the HHT method is the empirical mode decomposition (EMD), which can decompose any complicated data into some series of intrinsic mode functions (IMF). Then we can construct the Hilbert spectrum by transforming the instantaneous frequency of IMF. Though the HHT method is adaptive and efficient, which already has validity of application in many fields, there are still some deficiencies. One of those problems—the mode mixing will be discussed in this paper and a new improvement of the original EMD method, the ensemble empirical mode decomposition method (EEMD), will be introduced.Firstly, this paper reviews the basic theories of several time-frequency methods. The frequency resolution of four the TF methods, Short Time Fourier Transform (STFT), Wavelet Transform (WT), Wigner-Ville Distribution and Hilbert-Huang transform (HHT), has been experimentally compared here. And it is proved by the experiment that the HHT method is adaptive and has high decomposition efficiency, which has more advantages in analyzing the non-linear and non-stationary signal.Secondly, this paper discuss the mode mixing problem brought by original EMD and introduce a new approach that consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. The effect of the added white noise is to provide a uniform reference frame in the time-frequency space, so the different scale signals can be collated in the proper intrinsic mode functions. The EEMD is proved to be a substantial improvement over the original EMD by simulation.Finally, a signal simulation system is designed in the MATLAB GUIDE development platform. Through this system, we can understand HHT theory more deeply.