Fast approximation of nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

  • Authors:
  • J. Solé-Casals;C. Jutten;D. T. Pham

  • Affiliations:
  • Signal Processing Group, University of Vic, Vic, Catalonia, Spain;Laboratoire des Images et des Signaux, INPG, Grenoble Cedex, France;Laboratoire de Modélisation et de Calcul, IMAG, Grenoble Cedex, France

  • Venue:
  • Signal Processing
  • Year:
  • 2005

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Abstract

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied. We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that speed of the algorithm is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.