This paper presents a novel optimization method for blind multi-channel identification. The formulation of the optimal blind channel identification problem consists of three components: a least squares fitting term, and two regularization terms representing objective functions of the cross relation and the deterministic subspace methods, respectively. The proposed method is robust to noise since it does not separately compute the common system input and channel functions but to estimate them concurrently using the convolution model of the channels and channel input. Simulation results are demonstrated showing that the proposed method outperforms both the cross relation method and the deterministic subspace method.
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