Multiple-frequency signal denoising method based on sparse autoregressive model modeling

Abstract

The invention discloses a multiple-frequency signal denoising method based on sparse autoregressive model modeling. The method includes on the basis of a sparse autoregressive model, creating an adaptive overcomplete sparse base of a multiple-frequency signal according to sampling values of the multiple-frequency signal; extracting multiple discontinuous rows from the adaptive overcomplete sparse base optionally to form a plurality of redundant dictionaries; acquiring sparse mapping coefficient vectors of vectors, corresponding to the redundant dictionaries, on the corresponding redundant dictionaries by a orthogonal matching pursuit algorithm; averaging the sparse mapping coefficient vectors and taking an average vector as a coefficient needing to be used during signal restoration; combining a denoising result of the original multiple-frequency signal with a denoising result of an inverted signal of the original multiple-frequency signal to acquire a denoised restored signal. The multiple-frequency signal denoising method has the advantages of low calculation complexity, good denoising effect, and stable denoising effect under the condition of processing of signals with different signal to noise ratios.

Description

technical field [0001] The invention relates to a signal denoising method, in particular to a multi-frequency signal denoising method based on sparse autoregressive model (AR) modeling. Background technique [0002] Today, health checks for large buildings are generally done by collecting vibration signals on the building and analyzing the vibration signals to study the health status of large buildings. However, due to the influence of the external environment and the limitation of the acquisition equipment, the collected vibration signal will contain noise, so the collected vibration signal must be denoised first. [0003] At present, signal denoising processing methods mainly include wavelet denoising method, least squares denoising method, threshold denoising method based on EMD (Empirical Mode Decomposition, empirical mode decomposition), denoising method based on FFT (fast Fourier transform) , Median filter noise reduction method, sparse noise reduction method, etc. A...

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a multi-frequency signal denoising method based on sparse autoregressive model modeling, which has low computational complexity and good denoising effect, and the denoising effect when processing signals with different signal-to-noise ratios Stablize

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