Blind source separation method based on improved artificial bee colony algorithm

Abstract

The invention discloses a blind source separation method based on an improved artificial bee colony algorithm. The method includes the following steps that an observation signal X(k)=[x[1](k), x[2](k),..., x[M](k)]<T> is obtained by a source signal S(k)=[s[1](k), s[2](k),..., s[M](k)]<T> through a nonsingular hybrid matrix A; the obtained observation signal X (k) is sent to a preprocessing filter, and an output signal Z(k) of the preprocessing filter is obtained; the output signal Z(k) of the preprocessing filter is sent to a separation matrix W(k), and a separation signal Y(k) is obtained; an initial optimized separation matrix Wopt (0) of the W(k) is obtained through the improved artificial colony algorithm; after the initial optimized separation matrix Wopt (0) of the W(k) is obtained, the W(k) is updated. According to the method, the improved artificial bee colony algorithm is adopted for optimizing the blind source separation method NGA based on natural gradient so that the initial optimized separation matrix can be obtained, and then signal separation is performed through the initial optimized separation matrix. The method is low in convergence speed and small in crosstalk error and has wide application prospect in the aspects of wireless communication, image processing, voice signal processing and the like.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a blind source separation method based on an improved artificial bee colony algorithm. Background technique [0002] BSS (Blind Source Separation, Blind Source Separation) is one of the emerging research topics in the field of signal processing. Its main task is to recover the source signal only by the signal received by the sensor when the source signal and the mixing method are unknown. . In the blind separation method, for the adaptive blind source separation method, when a larger step size is used for signal separation, the convergence speed is fast, but the separation performance is poor; when a smaller step size is used, the separation performance can be maintained better, But the convergence rate is slow. In addition to the step size factor, the initial separation matrix is ​​also a factor that affects the convergence performance. In the prior art, the initial...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to overcome the defects of the traditional artificial bee colony algorithm and blind separation method, and provide a blind source separation method based on the improved artificial bee colony algorithm. The present invention first improves the traditional artificial bee colony algorithm ABC , and then use the improved artificial bee colony algorithm to optimize the initial separation matrix to obtain better blind separation performance

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