Multiple-input-multiple-output blind system identification based on cross-polyspectra
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01
Fast algorithms for mutual information based independent component analysis
IEEE Transactions on Signal Processing - Part I
Super-exponential algorithms for multichannel blind deconvolution
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Criteria for multichannel signal separation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixture of sources via anindependent component analysis
IEEE Transactions on Signal Processing
Quasi-nonparametric blind inversion of Wiener systems
IEEE Transactions on Signal Processing
Frequency domain blind MIMO system identification based on second and higher order statistics
IEEE Transactions on Signal Processing
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In this paper, we propose an approach to the multichannel blind deconvolution problem, based on the mutual information criterion and more generally on an appropriate system of estimating equations. Formulas for the quasi-Newton algorithm and the asymptotic covariance matrix of the estimator are provided. More interesting results have been obtained in the pure deconvolution case. By a clever parameterization of the deconvolution filter, the estimated parameters are asymptotically independent and explicit and simple formula for their variance are obtained. The quasi-Newton algorithm also becomes particularly simple. Simulation results showing the good performance of the methods are provided.