Source Separation Using Higher Order Moments

This communication presents a simple algebraic method for the extraction of independent components in multidimensional data. Since statistical independence is a much stronger property than uncorrelation, it is possible, using higher-order moments, to identify source signatures in array data without any a-priori model for propagation or reception, that is, without directional vector parametrization, provided that the emitting sources be independent with different probability distributions. We propose such a "blind" identification procedure. Source signatures are directly identified as covariance eigenvectors after data have been orthonormalized and non linearily weighted. Potential applications to Array Processing are illustrated by a simulation consisting in a simultaneous range-bearing estimation with a passive array.