Separation of polynomial post non-linear mixtures of discrete sources
B. Lachover, Arie Yeredor · IEEE/SP 13th Workshop on Statistical Signal Processing, 2005 · 2005
We consider the problem of blind estimation of the parameters of noisy non-linear mixtures of sources with unknown discrete alphabets. The nonlinear mixtures are modeled using the "post non-linear" model, in which the source signal undergo a linear mixture first, and then each mixed signal undergoes an unknown nonlinear transformation. The individual nonlinear transformations are modeled in this paper as second-order polynomials, whose parameters are unknown. Using the estimate-maximize algorithm, we derive estimators for all the unknown parameters. We also computed the Cramer-Rao lower bound for the estimation, to which the obtained mean squared estimation error is empirically compared.