Blind separation hybrid matrix estimation method based on synchronous compression short-time Fourier transform

Abstract

The present invention discloses a blind separation hybrid matrix estimation method based on synchronous compression short-time Fourier transform. The purpose of the invention is to improve the sparsity of the time frequency domain of the observation signal, improve the energy of the useful signal, further reduce the influence of noise, and correctly estimate the hybrid matrix. Subjecting to STFT synchronous compression conversion, the observation signal is converted from the time domain to the time frequency domain, and the energy of each time frequency point in frequency is rearranged to improve the energy aggregation of the signal to be analyzed in the time frequency plane, thereby increasing the sparsity in the time frequency domain and reducing the effects of noise. In the process of extracting a single source point, a sparse coding method is used to search for a time frequency point corresponding to the only one non-zero element of coding coefficients, so as to search for time frequency points located in the same 1-D subspace. By minimizing the l1 norm of the coding coefficients and constructing the minimal objective function to extract the single source point, the noise suppression function is further enhanced. Finally, the hybrid matrix estimation is implemented by the hierarchical clustering method.

Description

technical field [0001] The invention relates to the field of mechanical vibration signal and acoustic radiation signal processing, in particular to a blind separation mixing matrix estimation method based on synchronously compressed short-time Fourier transform. Background technique [0002] The vibration and noise of the mechanical system have an important impact on the performance and safety of the system. For example, if the vibration of the submarine propulsion system is too large, it is easy to be discovered by the enemy and interfere with its own sonar detection. If the vibration of the rocket engine is too large, it is easy to cause the satellites and instruments it carries to be abnormal. work etc. Therefore, it is very important to find the source of vibration and noise and take measures to reduce its influence. The observation signal detected by the sensor is often the superposition of multiple vibration sources, and in view of the complexity of the mechanical sys...

AI Technical Summary

Problems solved by technology

However, for mechanical systems, it is difficult to ensure that all vibration signals have sparsity in the time domain and frequency domain, and the mixing matrix estimation methods based on the time-frequency domain are mostly based on STFT and wavelet transform. These traditional time-frequency The time-frequency transformation obtained by the processing method has low energy aggregation, resulting in insufficient sparsity of complex mechanical signals in the time-frequency domain, which in turn leads to large errors in single-source point estimation. Under low signal-to-noise ratio conditions, the mixing matrix cannot even be estimated correctly

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