Blind Separation Of Same Frequency Signal Based On Array Antenna

With the rapid development of wireless communication,all kinds of complex signals are filled in the sky,which leads to the complex and diverse communication environment in which people live and many kinds of signals coexist.Therefore,it is necessary to eliminate the interference signals in the mixed signals and extract useful signals.At the same time,spectrum resources are also facing unprecedented challenges.Spectrum resources are limited,but broadband services are growing rapidly.In this case,the separation of mixed signals is difficult to achieve the traditional signal processing methods,so it is urgent to develop the blind separation technology of mixed signals.Through the research and analysis of blind separation of the same frequency signal,the basic theory of array antenna and blind separation algorithm,the optimization and improvement of Fast ICA algorithm and particle filter algorithm are introduced in detail.Firstly,the problems existing in the application of Fast ICA algorithm in the blind separation of the actual same-frequency signal,such as improper selection of initial value and weak independence of source signal,Fast ICA algorithm will have poor convergence,or even non-convergence,resulting in the separation of the same-frequency signal is not ideal,or even can not be separated.Fast ICA algorithm of iterative convergence using Newton iterative method,the iterative convergence is relatively slow,usually can not meet the real-time processing,optimized improvement to the original algorithm,the steepest descent method,introduced the first step,to deal with initial value,the second choice third-order two step convergence method,Newton iteration convergence of algorithm is optimized to improve.Finally in the MATLAB platform,carries on the same frequency signal blind source separation experiment,first set up three same frequency signals as the source signal and random noise signal all the way,four-way signal mixed to form mixed signal testing,the number of iterations,the iteration time,and the crosstalk error indicators as a contrast,comparative experiments show that the improved algorithm performance has been greatly promoted,It not only solves the problem of uneven iteration speed and sensitivity to initial value,but also makes the convergence of the algorithm more stable.Original particle filter algorithm is only applicable in high SNR or no noise environment,types of filter and symbol rate have special requirements,at the same time,the original particle filter algorithm based on multiple sampling,in same frequency signal blind source separation when there is interference signal linear amplification problems,at the same frequency signal blind source separation and can’t solve the problem of deviation of the same frequency signal nonlinear.Aiming at the above problems existing in the original algorithm,the original particle filter algorithm is optimized and improved.Received mixed signal by the same frequency,first choose no trace kalman filter for mixed signal to generate the important density function,extraction of particles closer to the posterior probability density function of distributed generation sample,makes the improved algorithm can better approximate posterior probability density distribution,secondly on the basis of the above improvements introduced markov chain monte carlo method increases the diversity of the sampling particles,As the likelihood function in the system state transition probability density function of the tail,or require separation results observation accuracy is high,can be better implementation,finally,the simulation experiment on the MATLAB platform to build two road along with frequency signal and random noise signal with the frequency of mixed signal model,carries on the original algorithm and improved algorithm separation effect,Under the condition of the same number of experiments,the average correlation coefficient and crosstalk error of the original particle filter algorithm and the improved particle filter algorithm are compared,and the improved algorithm is better than the original algorithm in blind separation of the same frequency signal to a certain extent.Finally,when the mixed signal-to-noise ratio is the same,the original algorithm is compared with the improved algorithm,and the improved particle filter algorithm is better than the original particle filter algorithm.At the same time,the two improved algorithms are applied in the actual environment,and can accomplish the task of blind separation of the same frequency signal well.Mixed with frequency signal blind source separation is the most challenging research studies in signal processing,the study of the mixed signal blind source separation has a broad application prospect,is of great significance in the signal processing research,study the frequency of mixed signal blind source separation is increasing in the current communication users,communications,spectrum is becoming more and more crowded conditions,an effective method to improve the utilization ratio of channel.