Blind Deconvolution of Correlated Sources Based on Second-Order Statistics

The blind deconvolution of signals composed of statistically dependent samples is an important practical problem whose understanding still requires the clarification of many theoretical points. In this work, we present an analysis of this problem that includes two well-established methods - the canonical constant modulus algorithm (CMA) and a correntropybased method - and two novel strategies that explore the temporal profile of the signal of interest. These techniques are compared in a number of representative scenarios, where it will be possible to form a clearer view of their potentialities and also of some peculiarities of the problem itself.