Blind identification using channel order estimation: subspace approach based on CGM

Subspace methods (SSM) are an effective approach for blind identification. However, these methods have two major disadvantages: i) they require a large amount of computation for the eigen-value decomposition (EVD) and the singular-value decomposition (SVD), what is more, ii) they require the prior knowledge of accurate channel order. In this paper, we discuss a new algorithm for blind identification using the property of conjugate gradient method (CGM) and using the conception of principal component analysis (PCA), which is based on the orthogonality between the subspaces spanned by the column vectors of the impulse response matrix (the impulse response subspace) and the noise subspace. The new algorithm does not need calculation of both EVD and SVD, and still more the prior knowledge of accurate channel order is unnecessary. Furthermore, the new algorithm has computations O(m/sup 2/) where m is the data vector length. We show the effectiveness of the proposed method by numerical example.