Blind Compensation of Nonlinear Distortions : Application to Source Separation of Post-Nonlinear Mixtures

Leonardo Tomazeli Duarte (1) , Ricardo Suyama (1) , Bertrand Rivet (2) , Romis Attux (1) , Joâo M. T. Romano (1) , Christian Jutten (2)

1 DSPCom - Laboratory of Signal Processing for Communications
2 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing

In this paper, we address the problem of blind compensation of nonlinear distortions. Our approach relies on the assumption that the input signals are bandlimited. We then make use of the classical result that the output of a nonlinear memoryless system has a wider spectrum than the one of the in- put signal. However, different from previous works, our approach does not assume the knowledge of the input signal's bandwidth. The proposed approached is considered in the development of a two-stage method for blind source separation (BSS) in post- nonlinear (PNL) models. Indeed, once the functions present in the nonlinear stage of a PNL model are compensated, one can apply the well-established linear BSS algorithms to complete the task of separating the sources. Numerical experiments performed in different scenarios attest the viability of the proposal. Moreover, the proposed method is tested in a real situation where the data are acquired by smart chemical sensor arrays.

Keywords: Bandlimited signals; blind source separation; nonlinear distortion; post-nonlinear model; smart chemical sensor arrays

Domains: Signal and Image processing; Signal and Image Processing

Leonardo Tomazeli Duarte, Ricardo Suyama, Bertrand Rivet, Romis Attux, Joâo M. T. Romano, et al.. Blind Compensation of Nonlinear Distortions : Application to Source Separation of Post-Nonlinear Mixtures. IEEE Transactions on Signal Processing, 2012, 60 (11), pp.5832-5844. ⟨10.1109/TSP.2012.2208953⟩. ⟨hal-00764832⟩