We propose a new approach for the blind identification of a multi-input-multi-output (MIMO)
system. As a substitute to using" classical" high-order statistics (HOS) in the form of time-
lagged joint cumulants, or polyspectra, we use the estimated Hessian matrices of the second
joint generalized characteristic function of time-lagged observations, evaluated at several
preselected" processing-points." These matrices admit straightforward consistent estimates,
whose statistical stability can be finely tuned (by proper selection of the processing-points) …

We propose a novel algorithm for the identification of a Multi-Input-Multi-Output (MIMO)
system. Instead of using “classical” high-order statistics, the mixing system is estimated
directly from the empirical Hessian matrices of the second generalized characteristic
function (GCF) at several preselected “processing points”. An approximate joint-
diagonalization scheme is applied to the transformed set of matrices in the frequency
domain. This yields a set of estimated frequency response matrices, which are transformed …