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Research On Blind Signal Separation Algorithm Based On Swarm Intelligence Optimization

Posted on:2012-05-26Degree:DoctorType:Dissertation
Country:ChinaCandidate:L ChenFull Text:PDF
GTID:1118330362953738Subject:Signal and Information Processing
Abstract/Summary:PDF Full Text Request
Blind signal separation technology is one kind of method for recovering source signal from observation signal and it has broad application prospect and development potential in various fields such as speech, image, communication and biomedicine. Research on blind signal separation technology has been a hotspot in signal processing and intelligence computation. Swarm intelligence optimization algorithm is a kind of optimization algorithm using the principle of survival behavior of organism in nature and it is an efficient method for solving complex optimization problem. Therefore, there is fine development prospect for using swarm intelligence optimization algorithm in blind signal separation.Linear mixture blind signal separation was researched on the basis of study on swarm intelligence optimization algorithm and principle of blind signal separation in this paper. The main work can be expressed as follows(1) A sequential blind signal separation algorithm based on particle swarm optimization was proposed. The absolute value of normalized fourth-order cumulant was used as objective function in the algorithm and particle swarm optimization algorithm was used for optimizing it. The separated source signal component was wiped off from mixed signal using decorrelation deflation method. Source signal can be separated efficiently according to the order of their absolute value descending of normalized fourth-order cumulant. Simulation results for separation on supergaussian signal, subgaussian signal and mixture of supergaussian signal and subgaussian signal show that the algorithm can achieve the sequential blind separation for various types of source signal efficiently. And then, the separation algorithm was used for power interference removal in weak signal collection and the property of interference removal is excellent.(2) A bacterial colony optimization algorithm based on detection determination strategy and random perturbation strategy was proposed. Based on bacterial chemotaxis optimization algorithm, detection determination strategy and random perturbation strategy combined with swarm behavior of bacteria and center attraction strategy was led in the process of bacterial colony evolution for solving the difficulty of locating on global optimal position. The convergence precision and global convergence property was improved. A sequential blind signal separation algorithm based on bacterial colony optimization algorithm was proposed and the algorithm can achieve the sequential blind separation for various types of source signal efficiently. Lastly, the sequential blind separation algorithm was used for solving the power interference removal in weak signal collection and the simulation results prove the validity of the method.(3) A blind signal separation algorithm based on bacterial foraging optimization algorithm was proposed. Sum of the absolute value of fourth-order cumulant was used as objective function in the separation algorithm. The number of unknown elements was reduced using Givens transform method and the modified bacterial foraging optimization algorithm was used for optimizing the objective function. All the source signal can be separated from the mixture signal simultaneously and the property of the separation is proved by simulation result. Lastly, the blind separation algorithm based on bacterial foraging optimization was used for solving the power interference removal problem and the property of interference removal is good.
Keywords/Search Tags:blind signal separation, swarm intelligence optimization, particle swarm optimization, bacterial chemotaxis, bacterial foraging optimization, power interference
PDF Full Text Request
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