Blind source separation method

A technology of blind source separation and separation matrix, applied in the field of signal processing, to achieve the effect of improving diversity, reducing time complexity, and shortening search time

Inactive Publication Date: 2016-03-16
JILIN UNIV
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  • Summary
  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is: Aiming at the problem that the blind source separation technology is easy to fall into the local optimal solution and premature, a parameter

Method used

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Embodiment 1

[0027] Embodiment 1, the mathematical model of the present invention.

[0028] refer to figure 1 , a kind of blind source separation method of the present invention, the mathematical modeling in the present embodiment is: be provided with n source signals, they form n-dimensional vector: S (t)=[s 1 ,s 2 ,...,s n ] T ,s i ∈C n for mutual statistics independent The source signal; mutual statistics between vectors independent , m mixed signals constitute an m-dimensional observation data vector: X(t)=[x 1 ,x 2 ,...,x m ] T , x i ∈C m is the actual observed signal; the mathematical model of blind signal separation with noise is:

[0029] X(t)=A·S(t)+n(t)(1)

[0030] In the formula, A is an m×n dimensional mixing matrix, and n(t) is an m×n dimensional additive noise.

[0031] The solution to the blind source separation problem is to find a separation matrix W while ignoring the noise n(t), so that the separated matrix Y(t) satisfies:

[0032]

[0033] Here kur...

Embodiment 2

[0036] Embodiment 2, overall steps of the present invention.

[0037] refer to figure 2 , a blind source separation method of the present invention, in the present embodiment, the overall steps of the present invention are:

[0038] Step 1: Preprocessing the observed signal.

[0039] The preprocessing includes centering and whitening the mixed signal, and centering is also called mean removal, which can be realized by the following formula:

[0040]

[0041] The whitening operation on the mixed signal is irrelevant between the signals, and the pre-whitening of the random vector x is through the whitening matrix T, which has so that the transformed vector The correlation matrix satisfies Is an identity matrix I, the second-order statistics between components after whitening independent .

[0042] Step 2: Initialize the particle swarm. Use the gradient formula to generate particle swarms, and initialize the parameters of particle swarms, including c 1 and c 2 T...

Embodiment 3

[0049] Embodiment 3, the steps of the chaotic particle swarm algorithm of the present invention.

[0050] refer to image 3 , a blind source separation method of the present invention, in the present embodiment, the specific steps of the chaotic particle swarm algorithm of the present invention are:

[0051] Step 1: Read the information of the part of the particle swarm assigned as excellent particles, which includes the initial position of each particle, the initial velocity, the initial parameter value of the particle swarm and the fitness of each particle.

[0052] Step 2: Adaptively generate learning factor c through the following formula 1 、c 2 and inertial weights ω,

[0053]

[0054] c 1 (t)=2.5-2*exp(-α|f avg -f g |)(6)

[0055] c 2 (t)=0.5+2*exp(-α|f avg -f g |)(7)

[0056] In the formula, iter max is the maximum number of evolutions. f g is the optimal fitness, f avg is the average fitness of the particle swarm, and α is the control coefficient. ...

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Abstract

The present invention provides a blind source separation method, and belongs to the technical field of signal processing. The method comprises three steps of: blind source problem problem modeling, fitness evaluation, and separation matrix solving. According to the blind source separation method provided by the present invention, a parameter-adaptive particle swarm algorithm is adopted, and chaotic iteration and a cloud model are introduced into the particle swarm algorithm, so that a particle swarm alternate between chaos and stability and gets close to an optimal solution, thereby effectively solving problems that solving a separation matrix in a blind source separation problem is prone to fall into a local optimum solution and a premature convergence problem, greatly reducing a search time, and reducing time complexity of the blind source separation.

Description

technical field [0001] The invention belongs to the field of signal processing, in particular to a blind source separation method based on cloud model and chaos parameter adaptive particle swarm algorithm. Background technique [0002] Blind Source Separation (BSS) is a new signal processing method developed with the re-emergence of neural networks in the 1980s. Its idea originated from people's research on "cocktail parties". When both the source signal and the transmission channel parameters are unknown, the process of recovering the source signal from the observed signal is based only on the statistical characteristics of the input signal. As the main method of blind source separation, Independent Component Analysis (ICA) includes objective function selection and optimization. Traditional ICA optimization uses the steepest gradient descent algorithm, which has problems such as slow convergence speed and easy to fall into local optimal solutions. The quality cannot be gua...

Claims

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Application Information

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IPC IPC(8): G06F19/00G06N3/00
CPCG06N3/00G16Z99/00
Inventor 于银辉陈倩张磊田小建王达周恒
Owner JILIN UNIV
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