Algorithms Of Blind Sources Separation In Frequency Domain

Blind source separation is a research hotspot in signal processing. Many algorithmshave been proposed based on different principles, the performance of blind sourceseparation algorithms has been inproved gradually. However, there are still manyproblems which need further researching. Based on the works by former researchers, afrequency-domain algorithm is proposed on the basis of the time-domain algorithmwhich is suitable for a simplified mixing model. Besides, the stability of the fastICAalgorithm is discussed and simulation results are presented.Depending on different mixed model, blind sources separation problem can bedivided into instantaneous mixing model and convolution hybrid model. According tothe implementation process of mixed model, the instantaneous mixture model is aspecial case of convolution hybrid model. So blind sources separation algorithm of theinstantaneous mixing model can be further extended to the algorithm of convolutionmixing model. This promotion can not only in the time domain but also in the frequencydomain. In view of the frequency domain algorithm output sequence uncertaintyproblems, blind source separation algorithm of integral objective function in thefrequency domain is proposed.The research work includes the following aspects:(1)Time domain block LMS algorithm implementation in frequency domainBased on the introduction of the time-domain LMS algorithm, this research givesDLMS blind sources separation algorithm. To overcome the shortcoming that theconsideration amount of time-domain DLMS algorithm is very large and the operationrate is very low. Using operational unit of a block instead of the point operation, theblock DLMS algorithm realized and implemented in the frequency domain. Then wecan get block DLMS blind sources separation algorithm in frequency domain.(2)Blind sources separation algorithm for convolutive mixture signals based on ParsevalIn order to overcome the problem of the output signal order in frequency domain,we introduce integral objective function blind sources separation algorithm of the powerspectral density matrix joint diagonalization and information theory. Under thedecorrelation equals the least mean squares, a new integral objective function blindsources separation algorithm is proposed which based on Paceville theorem.(3) Stability analysis of fastICAA large number of simulation experiments and theoretical analysis show that thefastICA algorithm has good separation performance and it always has many advantagesthan general ICA algorithm, but there has special situation of mixed signals can not beseparated. Based on the theoretical analysis of the stability on fastICA, the results showthat this algorithm exists partial separation point. Simulation verify the theoreticalanalysis.