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Blind Processing Algorithm Based on Probability Density Estimation

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Abstract

Most algorithms for resolving the problems of blind source separation (BSS) and blind deconvolution are mainly focused on blind separation for mixed signals using higher order statistics of random signals, including third-order (for sources with dissymmetrical distribution) and fourth-order cumulant (for sources with symmetrical distribution). Usually, this kind of method directly employs an algebraic structure of a mixed signal cumulant matrix. The common method makes eigenvalues decomposition to the cumulant matrix that is estimated by the mixed signal samples. It can also perform joint diagonalization for the cumulant matrix through complex matrix transformation to estimate the mixing matrix in order to solve the BSS problem.

Keywords

Probability Density Function Blind Source Separation Blind Deconvolution Probability Density Estimation Blind Separation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Shanghai Jiao Tong University Press, Shanghai and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Institute of Vibration Shock & NoiseShanghai Jiao Tong UniversityShanghaiChina

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