Research On Blind Source Separation Algorithm Based On Independent Component Analysis

Blind source separation,as an important means of communication against reconnaissance,has received more extensive attention in recent years.In-depth study of blind source separation technology has important military significance.In this paper,the blind source separation problem of linear instantaneous mixed communication signals in communication confrontation reconnaissance is studied.The blind source separation algorithm is deeply studied based on independent component analysis.The main research contents are as follows:(1)The basic principle of blind source separation based on independent component analysis is systematically expounded.The basic steps of independent component analysis method are summarized.Several common independence criteria in independent component analysis are introduced.The advantages and disadvantages of three common optimization algorithms are analyzed.Provide a theoretical basis for subsequent chapters.(2)Research on the estimation algorithm of source number.Aiming at the problem of estimating the failure in the colored noise by the information theory criterion algorithm,an improved algorithm is proposed,which is suitable for the color noise environment by the whitening noise of the diagonal loading technique.The problem of low estimation accuracy for the Gaelic criterion algorithm is combined with the information theory.The criterion proposes an improved algorithm,which performs matrix transformation through the constructed diagonal matrix,so that the azimuth resolution of the algorithm is higher.(3)Research on blind source separation algorithm based on natural gradient.The natural gradient algorithm is deduced and the performance of the natural gradient algorithm is analyzed.Aiming at the problems of low separation precision and slow convergence speed for the four conventional variable step sizes,a new variable step size algorithm is proposed.By estimating the mixing matrix,we obtain The estimated value of crosstalk error is used to adaptively control the change of step size.The simulation results show that the algorithm has faster convergence speed and stronger global optimization ability,which balances the convergence speed and separation precision to some extent.The contradiction,while the algorithm also has good separation performance at low SNR.(4)Research on blind source separation algorithm based on whale optimization.Aiming at the problem that the whale optimization algorithm has a slow convergence speed,an improved algorithm combining adaptive weight and simulated annealing strategy is proposed.The simulation shows that the convergence speed of the improved algorithm is improved and the calculation accuracy is greatly improved.The convergence speed and the blind source separation algorithm are improved.There is a contradiction in the separation accuracy.The improved whale algorithm is used for the blind source separation of linear instantaneous mixed signals,and the kurtosis of the separated signals is used as a cost function.The improved particle swarm optimization algorithm and the standard whale optimization algorithm are compared in the simulation.The results show that the improved algorithm has higher separation precision and faster convergence speed for blind signals.The improved whale optimization algorithm combined with principal component analysis is used for the orderly blind separation of linear instantaneous mixed signals.The reverse order of values separates the source signals that are subject to arbitrary distribution.(5)Designed blind source separation software.The software includes a signal generation module,a source number estimation module and a signal separation module.The effectiveness of the software is verified by experiments of simulated signals and measured signals.