Blind extraction of chaotic sources from mixtures with stochastic signals based on recurrence quantification analysis

Authors:
Diogo C. Soriano;Ricardo Suyama;Romis Attux
Affiliations:
Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering (FEEC), University of Campinas (UNICAMP), C.P. 6101, ZIP CODE 13083-970, Campinas, ...;Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas, Universidade Federal do ABC (UFABC), Santo André, SP, Brazil;Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering (FEEC), University of Campinas (UNICAMP), C.P. 6101, ZIP CODE 13083-970, Campinas, ...
Venue:
Digital Signal Processing
Year:
2011

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Abstract

This work aims to present a new method to perform blind extraction of chaotic deterministic sources mixed with stochastic signals. This technique employs the recurrence quantification analysis (RQA), a tool commonly used to study dynamical systems, to obtain the separating system that recovers the deterministic source. The method is applied to invertible and underdetermined mixture models considering different stochastic sources and different RQA measures. A brief discussion about the influence of recurrence plot parameters on the robustness of the proposal is also provided and illustrated by a set of representative simulations.