摘要
Bofill et al. discussed blind source separation （BSS） of sparse signals in the case of two sensors. However, as Bofill et al. pointed out, this method has some limitation. The potential function they introduced is lack of theoretical basis. Also the method could not be extended to solve the problem in the case of more than three sensors. In this paper, instead of the potential function method, a K-PCA method （combining K-clustering with PCA） is proposed. The new method is easy to be used in the case of more than three sensors. It is easy to be implemented and can provide accurate estimation of mixing matrix. Some criterion is given to check the effect of the mixing matrix A. Some simulations illustrate the availability and accuracy of the method we proposed.
Bofill et al. discussed blind source separation （BSS） of sparse signals in the case of two sensors. However, as Bofill et al. pointed out, this method has some limitation. The potential function they introduced is lack of theoretical basis. Also the method could not be extended to solve the problem in the case of more than three sensors. In this paper, instead of the potential function method, a K-PCA method （combining K-clustering with PCA） is proposed. The new method is easy to be used in the case of more than three sensors. It is easy to be implemented and can provide accurate estimation of mixing matrix. Some criterion is given to check the effect of the mixing matrix A. Some simulations illustrate the availability and accuracy of the method we proposed.
基金
supported by the National Natural Science Foundation of China for Excellent Youth(Grant No.60325310)
the Guangdong Province Science Foundation for Program of Research Team(Grant No.04205783)
the National Natural Science Foundation of China(Grant No.60505005)
the Natural Science Fund of Guangdong Province(Grant No.05103553)
the Specialized Prophasic Basic Research Projects of Ministry of Science and Technology,China(Grant No.2005CCA04100).
