Research Of Time-Frequency Analysis Base On Intrinsic Model Decomposition And Its Correlation Application

Non-stationary signal can be often observed in our daily life and scientific research.For those signals,they are generated suddenly and wither away in a limited time.However,traditional frequency-analysis like Fourier transform is applicable only when the components of the signal last forever and have frequencies that are constant.So,the time-frequency analysis has been introduced as a new method to analysis the non-stationary signal.The new method represents signals in a two-dimensional plane of time and frequency.Different kind of time-frequency technologies including Sort Time Fourier Transform and Wavelet Analysis has been well developed since then.Among them,the new proposed Hilbert-Huang Transform (HHT) is commonly considered as a breakthrough to time-frequency analysis.This thesis will focus on discussions and improvements base on it.Base on the summary and a relatively thorough analysis of HHT,The thesis point out its deficiencies:There is possibility of losing tiny components in the process of Empirical Mode Decomposition;Aliasing phenomenon arises if the characteristic scales of signal suddenly changed;And,HHT cannot analyze the real-time signal. That means it is unable to analyze the signal the same time it being provided. Against those deficiencies,several improvements have been proposed here:1) the thesis proposes the Cur-EMD algorithm which adopts curvature extremes instead of amplitude extremes as the characteristic scales in the decomposition.Comparing to the traditional EMD algorithm,Cur-EMD boasts a higher precision.2) The thesis defines the conception of Mutation Point and the way to detect it.Thanks to this conception,an improved decomposition method of HHT has been proposed in this thesis.The new method can effectively avoid the aliasing phenomenon caused by the sudden change of the characteristic scales.3) Traditionally,because of HHT involves the repeated spline interpolate of an entire signal,the signal that HHT analyzes must be integrated.This thesis gives a discussion of a new improved decomposition base on the notion of "divide and rule".In additional,the new method adopts Akima interpolate instead of spline interpolate.All those together make it possible for using HHT to analyze real-time signal.That is the algorithm can give the current time-frequency analysis result of a consecutive input signal after a limited delay.In this thesis we implement the algorithms mentioned above by programming(Matlab and Java),and make a series of numerical experimentation.Based on the clearly presented results comes after different Time-Frequency analysis methods,this thesis gives an analysis and proves the efficiency of the improved algorithm.Finally,this thesis introduces an application of the proposed Time-Frequency analysis technology methods in a project that the author participated in.In this project,the Time-Frequency analysis technology was mainly used in aspects of signal de-noising and texture analysis for images.