Minimum Noise Subspace Method for Second Order Blind Identification of Convolutive Mixtures
K.A. Meraim, Yingbo Hua · Information Sciences, Signal Processing and their Applications · 1996
This paper presents a new subspace based method for blind identification of a p-input/q-output system where q > p. This method exploits a minimum noise subspace (MNS) to retrieve the system impulse responses. The MNS method requires only q - p noise vectors as opposed to all noise vectors needed by a standard subspace (SS) method. It is shown that the MNS can be obtained in a parallel structure from a set of tuples (combinations) of system outputs that form a properly connected sequence (PCS). The PCS exploits with minimum redundancy the diversity among system outputs. The MNS method is much more efficient in computation than the SS method although the former is less robust to noise than the latter.