Signal extraction method

Abstract

The invention discloses a signal extraction method. The signal extraction method comprises the following steps: 1, synchronously storing to-be-processed signals; 2, processing the to-be-processed signals: processing the to-be-processed signal f(t) by using data processing equipment, wherein the process comprises the following steps: 201, performing signal sparse decomposition based on an optimization algorithm; 202, performing residual amount judgment: judging whether ||R<m>(t)||2 obtained after signal sparse decomposition is less than epsilon or not, and if the ||R<m>(t)||2 is greater than orequal to epsilon, entering the step 203, otherwise, entering the step 204; 203, performing best matching atom optimization; 204, performing signal reconstruction. The method is simple in step, reasonable in design, convenient to implement and good in use effect; the best matching atom is searched according to a signal sparse decomposition method based on the optimization algorithm, the signal extraction precision is judged through residual amount judgment, and best matching atom optimization is conducted according to the judgment result, so that the signal extraction speed can be greatly increased and the signal extraction precision can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of signal time-frequency analysis, and in particular relates to a signal extraction method. 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 s...

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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