A stochastic subspace algorithm for blind channel identification in noise fields with unknown spatial covariance

In this paper, the blind channel identification problem is formulated in a stochastic state space framework. Starting from a state space model, we introduce a preprocessing step based on two orthogonal subspace projections, derived from the theory of Van Overschee and De Moor (Subspace Identification for Linear Systems: Theory, Implementation, Applications, Kluwer Academic Publishers, Dordrecht, 1996). Using these orthogonal projections, we present an algorithm for blind channel estimation which is robust to the spatial color of the noise. (C) 2000 Elsevier Science B.V. All rights reserved.