Turbo source separation algorithm using HOS based inverse filter criteria

Ding and Ngugen proposed a kurtosis maximization algorithm and Chi arid Chen proposed a fast kurtosis maximization algorithm (FKMA) for blind separation of a instantaneous mixture of colored non-Gaussian sources. Their algorithms only involve spatial processing, but their performance may significantly degrade for finite signal-to-noise ratio as kurtosis magnitudes of source signals are not sufficiently large. This paper proposes a novel iterative blind source separation algorithm, called a turbo source separation algorithm (TSSA), which alternatively involves spatial processing as the FKMA, and temporal processing (blind deconvolution) using Chi and Cheri's fast inverse filter criteria algorithm at each iteration. Some simulation results are presented to support that the proposed TSSA works well with better performance than the FKMA and some existing second-order statistics based algorithms.