Wavelet threshold and EMD combined noise reduction method based on sparse decomposition

A technology of wavelet threshold and sparse decomposition, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of less large-scale coefficients, modal aliasing, weakening the sparsity of wavelet coefficients, etc., to improve noise performance, Improved noise suppression level, good effect of noise reduction performance

Active Publication Date: 2020-01-14
HARBIN ENG UNIV
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Problems solved by technology

Usually, the modal aliasing phenomenon will appear after LMD decomposes the signal, resulting in poor decomposition effect. Moreover, for most signals, selecting a suitable wavelet base and then using wavelet transform will obtain sparse wavelet coefficients, which contain less coefficient of magnitude
However, when the signal is disturbed by noise, the sparsity of wavelet coefficients after wavelet transform will be greatly weakened

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  • Wavelet threshold and EMD combined noise reduction method based on sparse decomposition
  • Wavelet threshold and EMD combined noise reduction method based on sparse decomposition
  • Wavelet threshold and EMD combined noise reduction method based on sparse decomposition

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specific Embodiment 1

[0061] according to figure 1 As shown, the present invention provides a joint noise reduction method based on wavelet threshold value and EMD of sparse decomposition, comprising the following steps:

[0062] Step 1: Use the EMD method to decompose the noisy signal s(t) to obtain the intrinsic mode function, and determine the original signal according to the intrinsic mode function;

[0063] EMD is based on the assumption that any signal can be composed of a finite number of Intrinsic Mode Function (IMF) components. After the signal is decomposed, a set of intrinsic mode components with inconsistent time scales are generated, so that each IMF is narrowband signal. At the same time, each IMF must meet the following two criteria:

[0064] a) In all data intervals, the number of extreme points and zero-crossing points of the function must be at most one difference or the same relationship;

[0065] b) At any point, the average value between the lower envelope generated by all t...

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Abstract

The invention relates to a wavelet threshold and EMD combined noise reduction method based on sparse decomposition. The method comprises the following steps: firstly, acquiring an intrinsic mode function IMF component of a signal by utilizing EMD decomposition; calculating the correlation between each IMF component and an original signal, determining an IMF noise frequency band according to the magnitude of the correlation, processing the noise frequency band by adopting a wavelet threshold denoising method, and finally reconstructing the processed signal to obtain a denoised signal. Simulation experiment analysis proves that the method has the capability of intelligently selecting the noise frequency band, and is a denoising method more suitable for torpedo signals. According to the invention, signal characteristics can be successfully reserved while noise reduction is carried out. The advantages of fast EMD operation and good noise reduction performance are retained, and the noise performance of each retained IMF is improved by adopting sparse wavelet threshold noise reduction, so that the noise suppression level is improved.

Description

technical field [0001] The invention relates to the technical field of joint noise reduction, and relates to a joint noise reduction method based on sparse decomposition of wavelet threshold and EMD. Background technique [0002] In signal processing, noise is the main factor that affects signal transmission, so it is necessary to separate noise and effective signal from the collected signal as much as possible to improve the signal-to-noise ratio and resolution. Noise is the most common interference wave in any signal, the causes of which are complex and diverse, and the form of noise is irregular vibration. Due to its unpredictable apparent speed, unfixed propagation direction, and uncertain frequency distribution range, it brings great challenges to separate effective signals from random noise. [0003] Empirical Mode Decomposition (EMD) realizes adaptive time-frequency processing based on its own data characteristics. After EMD decomposition, the signal can be decompos...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06F2218/06Y02D30/70
Inventor 马雪飞
Owner HARBIN ENG UNIV
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