Empirical mode decomposition denoising method based on revised wavelet threshold value

Abstract

The invention provides an empirical mode decomposition denoising method based on a arevised wavelet threshold value. The method is characterized by comprising the following steps of first carrying out the empirical mode decomposition on an original signal to acquire a plurality of intrinsic mode functions I with the frequency being gradually reduced and a remainder term; calculating the smoothness of each intrinsic mode function I; calculating a threshold value of each intrinsic function I by utilizing a wavelet threshold value method; revising the threshold value obtained through the wavelet method according to the smoothness and a serial number of each intrinsic mode function I; carrying out the soft threshold value treatment on each intrinsic mode function I by utilizing the revised threshold value to obtain an intrinsic mode function II; finally reconstructing the intrinsic mode function II to obtain a denoised signal. The method is good in self-adaptability, the threshold value calculated by adopting the wavelet threshold value method is revised through the smoothness index, a signal with high signal-to-noise ratio is obtained on the premise of guaranteeing the smoothness, and the method can be used for denoising the ultrasonic signal.

Description

technical field [0001] The invention belongs to the field of signal denoising, in particular to an empirical mode decomposition denoising method based on modified wavelet threshold. Background technique Signals inevitably introduce noise during generation and measurement. These noises are superimposed on the original signal, which interferes with the subsequent analysis and processing of the original signal. The research of many scholars revolves around the signal and has received positive results. Commonly used denoising methods include filter denoising, Fourier transform denoising and wavelet decomposition denoising. The Empirical Mode Decomposition (EMD) method is a signal analysis method proposed by Dr. E Huang of NASA. It decomposes the signal according to the time scale characteristics of the data itself, without presetting any basis function. Since the IMF components decomposed by the EMD method are arranged according to the frequency, according to this charact...

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Problems solved by technology

However, due to the essential difference between wavelet analysis and EMD, this method of directly applying the wavelet method to the EMD threshold denoising method has unstable denoising effect, easy to lose useful signals and the signal-to-noise ratio of the denoised signal is low

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