Wavelet threshold denoising parameter selection method based on composite evaluation index and wavelet entropy

Abstract

The invention aims to provide a wavelet threshold denoising parameter selection method based on a composite evaluation index and wavelet entropy, which comprises the following steps: acquiring a standby wavelet basis, carrying out wavelet denoising processing, calculating the composite evaluation index, respectively carrying out normalization processing on a data set consisting of two traditional evaluation indexes of different decomposition layers of each wavelet basis, and selecting a wavelet threshold denoising parameter according to the normalization processing. Each wavelet basis obtains a group of composite evaluation indexes, the optimal decomposition layer number is determined, the similar wavelet basis is used for carrying out layer-by-layer wavelet decomposition on a noisy signal to the optimal decomposition layer number, the optimal wavelet basis of each layer of decomposition is determined by calculating the wavelet entropy of a low-frequency coefficient, and the composite evaluation indexes are compared to determine the optimal denoising scheme in the multiple types of wavelet basis. According to the method, the optimal decomposition layer number is determined by constructing the composite evaluation index, and the optimal wavelet basis of each layer of decomposition is determined by calculating the low-frequency coefficient wavelet entropy, so that the optimization problem of two denoising parameters, namely the wavelet basis and the decomposition layer number, in wavelet denoising is solved on the premise that the calculation amount is reduced as much as possible.

Description

technical field [0001] The invention relates to a denoising method, specifically a wavelet threshold denoising method. Background technique [0002] Affected by many factors, observation data is always mixed with a lot of noise information, which brings difficulties to the extraction and identification of signal features. Therefore, signal denoising, which is one of the classic problems in the field of signal processing, is particularly important. The traditional denoising methods mainly include linear filtering and nonlinear filtering. Their disadvantages are that the entropy of the transformed signal increases, the non-stationarity of the signal cannot be described, and the correlation of the signal cannot be obtained. At present, in practical engineering applications, for unsteady signals whose true value is unknown, denoising methods mainly include wavelet denoising, Kalman filter denoising, particle filter denoising and curve threshold denoising, etc. The wavelet denoi...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a wavelet threshold denoising parameter selection method based on composite evaluation index and wavelet entropy, which solves the optimization problem of wavelet base and decomposition layer number in wavelet denoising under the premise of reducing the amount of calculation as much as possible

Images