A Signal Denoising Method Based on Time-Frequency Analysis

Abstract

The invention discloses a signal denoising method based on time-frequency analysis, comprising steps: 1. synchronously storing the signal to be processed; 2. denoising the signal: using data processing equipment to denoise the signal f(t) to be processed, the process is as follows : Step 201, signal sparse decomposition based on an optimization algorithm; when using a data processing device to search for a pair, the time-frequency parameter r of the pair n The process of searching is as follows: step C1, time-frequency parameter optimization; step C2, optimal time-frequency parameter determination; step 202, signal reconstruction. The method of the invention has simple steps, reasonable design, convenient implementation, and good use effect. The signal sparse decomposition method based on the optimization algorithm is used to search for the best matching atom, and at the same time, the best matching atom is determined by combining the fitness value and the sparsity, which can greatly speed up the process. Signal denoising speed, and can effectively improve the denoising effect, to ensure the accuracy of the signal after denoising.

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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