Signal dedrying method based on time-frequency analysis

Abstract

The invention discloses a signal dedrying method based on time-frequency analysis. The method comprises the steps that 1, a to-be-processed signal is synchronously stored; 2, signal dedrying is conducted, wherein data processing equipment is adopted for dedrying the to-be-processed signal f(t), the process comprises the substeps that 201, signal sparse decomposition based on an optimization algorithm is conducted; when the data processing equipment is adopted for seeking psirn(t), and the time-frequency parameter rn is sought through the following processes of C1, time-frequency parameter optimization; C2, optimal time-frequency parameter determination; 202, signal reconstruction is conducted. The method is simple in step, reasonable in design, convenient to implement and good use effect,a signal sparse decomposition method based on the optimization algorithm is adopted for seeking the optimal matching atom, and meanwhile a fitness value and sparsity are combined for determining the optimal matching atom; the signal dedrying speed can be greatly increased, the dedrying effect can be effectively improved, and the accuracy of the signal obtained after dedrying is guaranteed.

Description

technical field [0001] The invention relates to a signal denoising method, in particular to a signal denoising method based on time-frequency analysis. Background technique [0002] Time and frequency are the two most important physical quantities to describe a signal, and there is a close relationship between the time domain and the frequency domain of a signal. Time-Frequency Analysis (JTFA), short for Joint Time-Frequency Analysis, is a powerful tool for analyzing time-varying non-stationary signals and is an emerging signal processing method. The time-frequency analysis method provides the joint distribution information of the time domain (referred to as the time domain) and the frequency domain (referred to as the frequency domain), and clearly describes the relationship of the signal frequency with time. [0003] At present, most of the signals collected by the signal sampling system (also called signal acquisition system or signal acquisition equipment) are signals t...

AI Technical Summary

Problems solved by technology

However, the above-mentioned existing signal extraction methods, such as nonlinear filtering, Fourier transform and wavelet transform, etc., these methods have a good effect on improving the signal-to-noise ratio of general ultrasonic signals, but for small defects or defects under strong noise background The extraction has limitations, the test results are inaccurate, and the reliability is not high

However, the sparse decomposition algorithm has two defects. One is that the sparse decomposition algorithm has a large amount of calculation, and the calculation time is very large under the current computing conditions, so it cannot be detected in real time; the other is that the sparse decomposition algorithm is the optimal solution under continuous conditions. solution, the detection accuracy for weak and small defects is still limited

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