Signal combined denoising method based on empirical mode decomposition (EMD) and wavelet analysis

Abstract

The invention belongs to the field of signal processing technologies, and particularly relates to a denoising method of additive white gaussian noise signals at a low signal to noise rate. The method includes the steps that according to autocorrelation of signals, autocorrelation of the signals is solved, the autocorrelation functions of the signals obtain the maximum values at zero points, the amplitude change along with change of time difference and will not attenuate to a small value quickly; EMD is performed on the signals mixed with the gaussian white noise, due to the nature of EMD, the gaussian white noise is not the real white noise any more, however, the statistical property of the white noise approximately exists, in other words, the autocorrelation functions of all the signals mixed with the gaussian white noise obtain the maximum values at zero points, the amplitudes change along with change of the time difference and will attenuate to the small value quickly. Through the difference, IMF components of which the noise plays a main role are selected, and the influences of the noise on the signals can be effectively reduced. Under the condition of the low signal to noise rate, the denoising performance of the method is superior to that of a traditional method, and signal denoising can be completed under the condition of the low signal to noise rate.

Description

technical field [0001] The invention belongs to the technical field of signal processing, in particular to a denoising method for additive Gaussian white noise signals under low signal-to-noise ratio. Background technique [0002] Signals usually contain a lot of useful information, such as frequency characteristics, time characteristics, etc. The purpose of signal analysis is to transform some information characteristics of the signal into a form that is easy for people to understand by certain means, so as to better understand the physical characteristics represented by the signal. In order to extract useful information from the signal, people have studied a variety of transformation and analysis methods in order to better observe and analyze the signal from multiple angles. Most of the traditional signal processing methods are based on the assumption of linear and stable Gaussian signals, but many signals in real life and production work are mostly nonlinear and non-stat...

AI Technical Summary

Problems solved by technology

When the signal-to-noise ratio is low, since the useful information energy of the signal is very small, and the noise part becomes smaller with the increase of the number of decomposition layers, the threshold should be adaptive. Poor denoising performance compared to

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