Blind identification using second-order statistics: a nonstationarity and nonwhiteness approach

We consider an approach to the blind identification problem of instantaneous mixtures using second-order statistics through the nonstationarity and nonwhiteness properties of signals. We propose the use of natural gradient learning to form off-line/block processing (BP) and on-line processing (OP) algorithms suitable respectively for blind identification with batch data and on-line data and show that the proposed algorithms can be considered as a class of algorithms offering quasi-uniform performance. The identifiability conditions are presented which provide a key insight into these algorithms. The paper shows simulation results and concludes with some connections of the proposed algorithms to other existing algorithms.