Fuzzy entropy-based noisy signal processing method and iterative singular spectrum soft threshold denoising method

A noise-containing signal and processing method technology, applied in the field of iterative singular spectrum soft threshold denoising, can solve the problem that the signal-to-noise ratio cannot be significantly improved

Inactive Publication Date: 2018-02-13
DANYANG HUASHEN ELECTRIC APPLIANCE CO LTD
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It is like filtering out the high-frequency components of the...

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  • Fuzzy entropy-based noisy signal processing method and iterative singular spectrum soft threshold denoising method
  • Fuzzy entropy-based noisy signal processing method and iterative singular spectrum soft threshold denoising method
  • Fuzzy entropy-based noisy signal processing method and iterative singular spectrum soft threshold denoising method

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[0027] All features disclosed in this specification, or steps in all methods or processes disclosed, may be combined in any manner, except for mutually exclusive features and / or steps.

[0028] Any feature disclosed in this specification (including any appended claims, abstract and drawings), unless expressly stated otherwise, may be replaced by alternative features which are equivalent or serve a similar purpose. That is, unless expressly stated otherwise, each feature is one example only of a series of equivalent or similar features.

[0029] Fuzzy entropy is a chaotic invariant used to characterize system complexity in chaos theory. Here, we first propose the concept of fuzzy entropy spectrum and use this spectrum to obtain the real noise plane. Whether or not a flat singular spectrum exists, the fuzzy entropy spectrum can reveal the relative noise level of each signal component and identify whether the component is signal or noise dominant.

[0030] When SSA decomposes a ...

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Abstract

The invention discloses a fuzzy entropy-based noisy signal processing method and an iterative singular spectrum (SSA) soft threshold denoising method. The method is suitable for noisy signals. Assuming that the noisy signal of length N xin = {x1, x2, ..., xN} and assuming that the additive white noise therein is uncorrelated with the signal, a d-dimensional vector is constructed and the similarityand fuzzy probability are defined by utilizing an original signal xin; a (d + 1)-dimensional vector is constructed and the corresponding similarity and fuzzy probability are defined by the same method; and the fuzzy entropy is defined in the drawing of the description. For components obtained by utilizing a known signal decomposition method, the singular spectrum distribution of all the components is defined as a fuzzy entropy spectrum. The fuzzy entropy for quantifying the complexity of the system in a chaos theory is utilized to characterize a noise plane and provide a more effective path for the processing of the noisy signal; the fuzzy entropy spectrum-based iterative singular spectrum (SSA-IST) soft threshold denoising method has the denoising performance better than that of the traditional truncated singular spectrum method, and wavelet transform and empirical mode decomposition denoising method.

Description

technical field [0001] The invention relates to signal denoising and filtering, in particular to a noise-containing signal processing method based on fuzzy entropy and an iterative singular spectrum soft threshold denoising method. Background technique [0002] Singular Spectrum Analysis (SSA) is an advanced signal processing method that combines classical time series analysis, linear algebra, multivariate statistics, and dynamical systems. The purpose of SSA is to decompose the signal to be analyzed into the sum of multiple physically meaningful components, such as trend, oscillating components and noise. According to these components, researchers propose different denoising, change point detection, missing value imputation, synchronization detection, feature extraction and prediction algorithms. In order to filter out noise, the method first calculates the eigenvalues ​​of the delay covariance matrix and arranges them in descending order. These eigenvalues ​​may form a re...

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Application Information

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IPC IPC(8): G01R23/165
CPCG01R23/165G06F17/18G06F17/16H03H17/0219H03H17/0255H03H2222/02
Inventor 谢洪波
Owner DANYANG HUASHEN ELECTRIC APPLIANCE CO LTD
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