Estimación de funciones no lineales en mezclas post-no lineales

Abstract

This paper proposes a new method for blindly inverting a nonlinear mapping which transforms a sum of random variables. This is the case of post-nonlinear (PNL) source separation mixtures. The importance of the method is based on the fact that it permits to decouple the estimation of the nonlinear part from the estimation of the linear one. Only the nonlinear part is inverted, without considering on the linear part. Hence the initial problem is transformed into a linear one that can then be solved with any convenient linear algorithm. The method is compared with other existing algorithms for blindly approximating nonlinear mappings. Experiments show that the proposed algorithm outperforms the results obtained with other algorithms and give a reasonably good linearized data