Research On BlindSource Separation Problem Based On Swarm Intelligence Algorithms

Blind source separation(BSS)is to separate the source signal from thereceived signals withoutany prior knowledge of the source signal and thetransmission condition.As a new technique in digital signal processingfield,BSS has very important theoretic meaning and practicalvalue.BSShasbeenwidelyusesignalprocessing,wireless communication,noise-elimination,biomedicinesignal processing,earthquake signalprocessing,image signal processing and other fields.Blind source separation has some problems at present.Linear mixture blind signal separation was researched on the basis of study onswarm intelligence optimization algorithm and principle of blind signal separation inthis paper.The main work can be expressed as follows:(1)Summarizing the origin of BSS and the research progress at homeand abroad,the paper introduces the fundamental principle of BSS and simply summarizes the current algorithms.It also analyzes the basic postulates of BSS problems and typical evaluating index of BSS algorithmperformance.(2)To solve the problem that the original particle swarm algorithm is easy to fall into the local optimum,an adaptive particle swarm optimization algorithm based on mutation operator was proposed.The absolute value of Negentropywas used as objective function in the algorithm and improved particle swarm optimizationalgorithm was used for optimizing it,Source signal canbe separated efficiently.Through simulation for separation on sub Gaussian signal and mixture of super-Gaussian signal and sub-Gaussian signal,similarity coefficient andSignaltoNoiseRatio of two performance evaluation criteria compared with basic particle swarm algorithm,the result demonstratethevalidityofthenew method.(3)To overcome the shortcoming of the original imperialist competitive algorithm is easy to fall into local optimum,animperialist competitive algorithm optimization algorithm based on Chaos theory was proposed.The value of mutual information was used as objective function in the algorithm and improved imperialist competitive algorithm was used for optimizing it.Through similarity coefficient andSignaltoNoiseRatio of two performance evaluation criteria compared with the original algorithm,test result shows the better global searching ability.