Abstract
An algorithm for the subspace approach to blind identification of multichannel (or single-input and multiple-output) FIR systems is proposed. The subspace approach requires the so-called noise subspace spanned by some eigenvectors of the correlation matrix of observations. In this paper, it is shown that a subspace of the noise subspace can be obtained by one-step scalar-valued linear prediction and is sufficient for blind identification. In place of eigenvalue decomposition, the proposed algorithm utilizes the linear prediction and hence is computationally effective. Computer simulations are presented to compare the proposed algorithm with the original one.
