An Adaptive Acquisition Method of Sparse Matrix of Vibration Signal

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 vibration signals. Background technique [0002] Compressed Sensing (CS) theory, as a new theoretical framework for signal description and processing, "compresses while sensing" unknown signals, successfully breaking through the bandwidth limitation of Shannon's law during sampling. The realization of compressed sensing theory includes three key elements: sparsity, uncorrelated observations, and nonlinear optimization reconstruction. Among them, the sparsity of the signal is a necessary condition for compressed sensing, uncorrelated observations are the key to compressed sensing, and nonlinear optimization reconstruction is Compressive sensing means to reconstruct the signal. In compressed sensing, sparse representation boils down to the construction of sparse matrix Ψ (sparse dictionary). From different perspectives, sparse dicti...

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Problems solved by technology

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

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