A Multi-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] Nowadays, the health inspection of large buildings generally involves collecting vibration signals on the buildings and analyzing the vibration signals to study the health status of large buildings. However, due to the influence of the external environment and the limitations of the collection equipment, the collected vibration signals will contain noise, so the collected vibration signals must be denoised first. [0003] At present, signal denoising processing methods mainly include wavelet denoising, least squares denoising, EMD (Empirical Mode Decomposition, Empirical Mode Decomposition) threshold denoising method, and FFT (Fast Fourier Transform) based denoising method , Median filter noise reduction method, sparse noise reduction method, etc. Among the above-ment...

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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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