A Sparsity-Based Method for Blind Compensation of a Memoryless Nonlinear Distortion: Application to Ion-Selective Electrodes

Leonardo Tomazeli Duarte (1) , Ricardo Suyama (1) , Romis Attux (1) , João Marcos 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 propose a method for blind compensation of a memoryless nonlinear distortion. We assume as prior information that the desired signal admits a sparse representation in a transformed domain that should be known in advance. Then, given that a nonlinear distortion tends to generate signals that are less sparse than the desired one, our proposal is to build a compensating function model that gives rise to a maximally sparse signal. The implementation of this proposal has, as central elements, a criterion built upon an approximation of the 0-norm, the use of polynomial functions as compensating structures, and an optimization strategy based on sequential quadratic programming. We provide a theoretic analysis for an 0-norm criterion and results considering synthetic data. We also employ the method in an actual application related to chemical analysis via ion-selective electrode arrays.

Mots clés: Index Terms— Nonlinear distortion; sparsity; ion-selective electrodes; chemical sensor arrays; blind methods

Domaines: Traitement du signal et de l'image [eess.SP]

Leonardo Tomazeli Duarte, Ricardo Suyama, Romis Attux, João Marcos Romano, Christian Jutten. A Sparsity-Based Method for Blind Compensation of a Memoryless Nonlinear Distortion: Application to Ion-Selective Electrodes. IEEE Sensors Journal, 2015, 15 (5), pp.2054-2061. ⟨10.1109/JSEN.2014.2371013⟩. ⟨hal-01164752⟩