Source separation using higher order moments

The author 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 parameterization, provided that the emitting sources are independent with different probability distributions. The author proposes such a blind identification procedure. Source signatures are directly identified as covariance eigenvectors after data have been orthonormalized and nonlinearly weighted. Potential applications to array processing are illustrated by a simulation consisting of a simultaneous range-bearing estimation with a passive array