摘要
Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions.So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions.Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture model.By using maximum likelihood(ML)approach,the convergence of the proposed algorithm is improved.The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.
Since in most blind source separation （BSS） algorithms the estimations of probability density function （pdf） of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model, they may not be efficient to separate sources with different distributions. So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS, the generalized Gaussian model （GGM） is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions. Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources, so it is less complex than Gaussian mixture model. By using maximum likelihood （ML） approach, the convergence of the proposed algorithm is improved. The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.
