In this paper a blind MIMO space-time equalizer is described, dedicated to convolutive mixtures when observations have been pre-whitened. Filters preserving space-time whiteness are paraunitary; a parameterization of such filters is proposed. Theoretical developments then lead to a numerical algorithm that sweeps all pairs of delayed outputs. This algorithm involves the solution of a polynomial system, whose coefficients depend on the output cumulants. Simulations and performance of the numerical algorithm are reported.

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Blind source extraction aims at estimating the source signals which appear mixed at the output of a sensor array. A novel approach to blind source extraction is presented in this contribution, which exploits the discrete character (finite alphabet property) of digital modulations in the case where sources with different alphabet exist. An alphabet polynomial fitting (APF) criterion matched to the specific signal constellation is employed to extract, through deflation, the sources with the same modulation. Using the appropriate APF criteria, the sources with different modulations can be extracted in parallel. This new concept, referred to as parallel deflation, presents the potential of reducing both the signal estimation errors that typically accumulate in the conventional deflationary approach and the spatiotemporal diversity required for a satisfactory source extraction. In addition, APF criteria can be optimized through a cost-effective optimal step-size technique that can escape local extrema.

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