Self-adaptive acquisition method for sparse matrix of vibration signals

Abstract

The invention discloses a self-adaptive acquisition method for a sparse matrix of vibration signals. The method comprises the following processing procedures: performing Nyquist uniform sampling on continuous vibration signals to obtain a priori signal; constructing a sparse matrix by utilizing each sampling value in the priori signal; applying the sparse matrix to compressive sensing theory to perform signal restoring, and updating the sparse matrix by utilizing restored signal; and then determining whether the updating of the sparse matrix is completed according to the signal-to-noise ratio of the restored signal. The method has the advantages that intrinsic characteristics of the vibration signals can be effectively represented through the configuration of the obtained sparse matrix, the sparsity of the vibration signals on the sparse matrix is enabled to be more obviously concentrated, and the sparsity restoring of the vibration signals can be better performed, so that the compressive sensing accuracy is improved; and the obtained sparse matrix can be adapted to local scope change of objects better, so that the signal sparsity restoring process can be quickly adjusted, and higher restoration precision is kept.

Description

technical field [0001] The invention relates to a signal processing technology, in particular to an adaptive acquisition method of a sparse matrix of a vibration signal. Background technique [0002] As a new theoretical framework for signal description and processing, Compressed Sensing (CS) theory successfully breaks through the bandwidth limit of Shannon's law when sampling unknown signals by "compressing while sensing". The realization of compressed sensing theory includes three key elements: sparsity, non-correlated observations, and nonlinear optimization reconstruction. The sparsity of the signal is a necessary condition for compressed sensing, uncorrelated observations are the key to compressed sensing, and nonlinear optimal reconstruction is A means of compressive sensing to reconstruct signals. In compressed sensing, sparse representation boils down to the construction of a sparse matrix Ψ (sparse dictionary). From different perspectives, sparse dictionaries can ...

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Problems solved by technology

[0003] The traditional sparse matrix construction method is to use general basis functions such as DCT (Discrete Cosine Transform, discrete cosine transform) basis and wavelet basis. The sparse matrix constructed by this traditional sparse matrix construction method is used for sparse sampling and restoration of signals. It has a good effect in general signal and image processing and has been widely used. However, the sparse matrix constructed by this traditional sparse matrix construction method (for example: DCT sparse matrix of orthogonal transformation) has a narrow application range. The anti-noise ability is weak, and for signals with strong nonlinearity and rapid local changes, the effect of signal restoration using this traditional sparse matrix will decrease

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