Blind Separation of Complex Sources Using Generalized Generating Function

We propose a new blind separation approach based on the Generalized Generating Function (GGF) of observations for complex sources by generalizing the definition of generating function. A new core equation is obtained and an approximate joint diagonalization scheme is used to estimate the mixing matrix by diagonalizing the Hessian matrix of the second GGF of the observations. Simulation results show that the GGF approach has superior performance to the existing classical algorithms when the SNR of observations is low and the data block is short.