Signal separation method based on particle swarm optimization

A particle swarm optimization and signal separation technology, which is applied in the field of signal processing and can solve problems such as inability to achieve accurate separation, detail deviation, and failure.

Inactive Publication Date: 2013-12-04
CHANGAN UNIV
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Problems solved by technology

[0004] The above methods in the prior art have a good application effect on conventional blind signal separation problems, but for unconventional problems such as super-Gaussian source mixed with sub-Gaussian source, strong source signal mixed with weak source signal, etc., the application effect is not ideal: for super-Gaussian source In the cas...

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  • Signal separation method based on particle swarm optimization
  • Signal separation method based on particle swarm optimization
  • Signal separation method based on particle swarm optimization

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[0054] The present invention will be described in further detail below in conjunction with the accompanying drawings:

[0055] Reference figure 1 The specific implementation process of the present invention is as follows:

[0056] Step 1. Input the observation signal.

[0057] Step 2. To minimize the difference between the joint probability of the separated signal and the simple product of the edge probability as the optimization objective, establish the independent component analysis algorithm optimization model shown below to find the optimal separation matrix:

[0058] Minimize | P ( y 1 , y 2 · · · , y m ) - Π i = 1 m P ( y i ) | w . r . t . W

[0059] s.t.:y(t)=Wx(t)

[0060] Among them: x(t) is the n-dimensional observation signal, y(t) is the m-dimensional separation signal, W is the separation matrix, P(y 1 ,y 2 ,···,Y m ) Is the separated signal y 1 ,y 2 ,···,Y m The joint probability of P...

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Abstract

The invention discloses a signal separation method based on particle swarm optimization. The method includes the steps: (1) inputting observation signals; (2) building an independent component analysis algorithm optimization model by taking a difference value between simple products of a minimum separated signal joint probability and a marginal probability as an optimization target; (3) estimating the number of source signals according to a singular value decomposition method and determining the number of optimization variables according to the number of the source signals; (4) calculating correlation coefficients of the observation signals, and determining the value range of the optimization variables; (5) optimizing separation matrixes by the aid of a particle swarm optimization algorithm; (6) taking particles with optimal fitness in the last generation population after optimization as an optimal separation matrix and multiplying the optimal separation matrix with the mixed signals to obtain the optimal separation signals. The method based on independent component analysis of the particle swarm optimization has universal applicability and effectively solves various blind source separation problems.

Description

Technical field [0001] The invention belongs to the technical field of signal processing, and relates to a signal separation method, especially a signal separation method based on particle swarm optimization. Background technique [0002] In many signal processing applications, the observation signal collected by the sensor is often a mixed signal formed by multiple sources, and the signal quality is poor. In order to reduce noise interference and improve signal quality, independent component analysis (ICA) can be used to process the observed signal and recover the independent source signal. [0003] In the prior art, many algorithms have emerged to solve the ICA problem. The most classic algorithm is the FastICA algorithm, which has the advantage of fast convergence speed. Compared with gradient-based algorithms, there is no need to choose a step size. The literature uses FastICA algorithm when solving the BSS problem. The Infomax algorithm proposed by Bell and Sejnowski in 1995...

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Application Information

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IPC IPC(8): H03H21/00
Inventor 李良敏高强房宏威冯帆杨本波周劲草
Owner CHANGAN UNIV
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