On blind MIMO system identification based on second-order cyclic statistics

Authors:
K. Sabri;M. El Badaoui;F. Guillet;A. Adib;D. Aboutajdine
Affiliations:
Laboratoire d'Analyse des Signaux & des Processus Industriels, Universitéé Jean Monnet, Inst., Universitaire de Tech., de Roanne, Roanne, France and Laboratoire GSCM-LRIT, Faculté d ...;Laboratoire d'Analyse des Signaux & des Processus Industriels, Universitéé Jean Monnet, Institut Universitaire de Technology de Roanne, Roanne, France;Laboratoire d'Analyse des Signaux & des Processus Industriels, Universitéé Jean Monnet, Institut Universitaire de Technology de Roanne, Roanne, France;Laboratoire GSCM-LRIT, Faculté des Sciences, Université Mohammed V, Rabat, Morocco;Laboratoire GSCM-LRIT, Faculté des Sciences, Université Mohammed V, Rabat, Morocco
Venue:
Research Letters in Signal Processing
Year:
2008

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Abstract

This letter introduces a new frequency domain approach for either MIMO System Identification or Source Separation of convolutive mixtures in cyclostationary context. We apply the joint diagonalization algorithm to a set of cyclic spectral density matrices of the measurements to identify the mixing system at each frequency up to permutation and phase ambiguity matrices. An efficient algorithm to overcome the frequency dependent permutations and to recover the phase, even for non-minimum-phase channels, based on cyclostationarity is also presented. The new approach exploits the fact that each input has a different and specific cyclic frequency. A comparison with an existing MIMO method is proposed.