Weak feature extraction method based on combination of singular value decomposition and correlation entropy theory

Abstract

The invention discloses a weak feature extraction method based on combination of singular value decomposition and a correlation entropy theory. The method comprises a singular value decomposition module, a correlation entropy induction measure calculation module, a signal reconstruction module and a cyclic correlation entropy spectrum estimation module. The method comprises the following steps: firstly, performing two-dimensional conversion on signals to obtain a Hankel matrix, and performing singular value decomposition on the Hankel matrix formed by the signals to obtain singular values and corresponding singular components; then calculating a correlation entropy induction measurement value of the original signal and each singular component, determining a threshold value according to the distribution of the correlation entropy induction measurement values, and selecting the singular component of which the correlation entropy induction measurement value is lower than the threshold value; performing signal reconstruction on the selected singular component to obtain a noise-reduced signal; finally estimating the cyclic correlation entropy spectrum of the reconstructed signal, and observing the signal characteristics. The method can effectively discriminate the singular components, and has a good noise reduction effect, thereby extracting the weak features of the signals.

Description

technical field [0001] The invention belongs to the technical field of signal noise reduction and feature enhancement, in particular to a weak feature extraction method based on the combination of singular value decomposition and correlation entropy theory. Background technique [0002] Singular value decomposition, as a simple, easy-to-implement, and good noise reduction algorithm, has attracted a lot of attention from researchers, and has been widely used in fault diagnosis, damage detection and other fields. There are also many attempts to improve the singular value decomposition algorithm. Among them, the selection of singular components has a great influence on the noise reduction processing of the signal. The selection of appropriate singular components will bring better noise reduction effect, and the selection of inappropriate singular components will lead to the loss of useful signals in the signal. [0003] At present, the singular value difference spectrum is wide...

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Problems solved by technology

Among them, the selection of singular components has a great influence on the noise reduction processing of the signal. The selection of appropriate singular components will bring better noise reduction effect, and the selection of inappropriate singular components will lead to the loss of useful signals in the signal.

[0003] At present, the singular value difference spectrum is widely used to select the singular component, such as the patent "an electrostatic monitoring device based on spectral interpolation and singular value difference spectrum" applied by Nanjing Forestry University (application number: CN201920307202.9); China University of Geosciences ( The patent "A Ground Penetrating Radar Noise Suppression Method Based on Hankel Matrix Singular Value Decomposition" (Application No.: CN201810339586.2) applied by Wuhan) is based on the singular decomposition of the differential spectrum for analysis. The neglected problem is, When the noise in the signal is large enough or the amplitude of the useful signal is relatively small, the difference between the signal and the noise cannot be reflected by the singular value difference spectrum. Denoising the signal only through the difference spectrum will lead to the loss of the useful signal

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