Hilbert-Huang Transform And Its Application In Signal Processing

In modern signal processing, non-linear, non-Gaussian and non-stable signals are usually the analysed and processed objects, especially non-stable signals. The conventional ways to analyze and process non-stable signals are: short time Fourier Tansform, Wigner-Ville distribution, Wavelet Transform and so on. But the above three algorithms are all based on Fourier Transform, so they all have the shortcomings of Fourier Analysis and can not get rid of the localization of it.Hilbert-Huang Transform is a new non-stable signal processing technology, proposed by N.E.Huang in 1998. It is composed of Empirical Mode Decomposition (referred to as EMD) and Hilbert Spectral Analysis (referred to as HSA). After EMD processing, any non-stable signal will be decomposed to a series of data sequences with different eigenscales. Each sequence is called an Intrinsic Mode Function (referred to as IMF). And then the energy distribution plot of the original non-stable signal can be found by summing all the Hilbert spectrums of each IMF. In essence, this algorithm makes the non-stable signals become stable and decomposes the fluctuations and tendencies of different scales by degrees and at last describes the frequency components with instantaneous frequency and energy instead of the total frequency and energy in Fourier Spectral Analysis. In this case, the shortcoming of using many fake harmonic waves to describe non-linear and non-stable signals in Fourier Transform can be avoided.This paper researches in the following parts:First, this paper deeply researches on the basic realization principles of Hilbert-Huang transform and confirms its validity by simulations. HHT can decompose the signal to a series of useful, physical meaningful IMF components, and calculate the instantaneous frequencies and amplitudes of IMF components by Hilbert transform to get Hilbert spectrum of the signal.Second, combing with amplitude normalization method, this paper proposes the automatic recognition algorithm for modulations signals based on HHT algorithm. When recognizing AM,FM,2ASK,2FSK and 2PSK five kinds of signals, this paper first makes use of character parameterγto classify amplitude modulation and non-amplitude modulations, and it can recognize AM and 2ASK well. Then, this paper takes advantage of HHT to get character parametersβandα, so that FM,2FSK and 2PSK can be recognized. When signal-noise-ratio (referred to as SNR) is beyond 10dB, the results are well. Third, this paper separately uses ARIMA model, LS and EMD to the elimination and extraction of tendency parts and makes comparisons. The results show that compared with the other two algorithms, EMD algorithm can adaptively extract tendency parts, and is easy to realize. This paper applies the tendency parts extracted by EMD algorithm to stock analysis and can judge the tendency of stock well.Fourth, this paper separately uses EMD scale filter algorithm, EMD threshold algorithm and EMD with wavelet algorithm to remove the noise in the simulation signals and makes comparisons. And then these three algorithms ate applied to remove the noise in EP signals. The results are good and EMD is the simplest and best one.