Underdetermined blind source separation method based on AP clustering

Abstract

The invention discloses an underdetermined blind source separation method based on AP clustering, and belongs to the field of signal processing, and the method comprises the following steps: (S1) converting an observation signal to a time-frequency domain, so as to screen out a time-frequency single source point; (S2) sampling the time-frequency single source points, and clustering the sampled time-frequency single source points by using an AP clustering algorithm to obtain the number of signal sources and an initial clustering center; (S3) taking the number of the signal sources, the initial clustering center and the time-frequency single source points obtained in the step (S1) as input of a preset target clustering algorithm, correcting the initial clustering center to obtain a corrected clustering center, and estimating a hybrid matrix by taking the corrected clustering center as a column vector of the hybrid matrix; (S4) reconstructing a signal source by using the hybrid matrix, wherein the input parameters of the target clustering algorithm comprise a clustering number and an initial clustering center. According to the invention, the estimation precision of the hybrid matrix in the underdetermined blind source separation system can be improved, so that accurate separation of source signals is realized.

Description

technical field [0001] The invention belongs to the field of signal processing, and more specifically relates to an underdetermined blind source separation method based on AP clustering. Background technique [0002] Blind source separation technology is a communication signal processing technology that can separate source signals from positional mixed signals. With the development of information technology in modern society, blind source separation technology has been widely used. In terms of fault diagnosis, blind source separation can be used to locate faulty equipment; in terms of image processing, blind source separation can be used to restore the original image from the image mixed with interference; in biomedicine, blind source separation can be used to complete the extraction of human physiological data or categories. [0003] In the blind source separation system, the source signal is equivalent to the unknown, and the observed signal is equivalent to the constrain...

AI Technical Summary

Problems solved by technology

In mixing matrix estimation, commonly used clustering methods include K-means clustering and fuzzy C-means clustering. These two clustering algorithms are fast and simple, and have good convergence, but these clustering algorithms are sensitive to the initial cluster center. And the number of source signals needs to be given, but in practical applications, the number of source signals in the underdetermined blind source separation system is usually unknown, so the existing underdetermined blind source separation method, the estimated mixing matrix accuracy is not high , the separation effect of the source signal is poor

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