Go To Program 117 Supervised Identification and Removal of Common Filter Components in Adaptive Blind SIMO System Identification Mark Thomas 1 Nikolay Gaubitch 1 Emanuel Habets 1 Patrick Naylor 1 1 Electrical and Electronic Engineering, Imperial College London, London, United Kingdom Adaptive blind system identification with LMS-type algorithms is prone to misconvergence in the presence of noise. In this paper we consider the hypothesis that such misconvergence is due to the introduction of a common filter to the estimated impulse respones. A technique is presented for identifying and removing the common filter using prior knowledge of the true channels. Experimental results with this approach show an improved rate of convergence and reduced system error. Furthermore, misconvergent behaviour is no longer observed, offering a plausible explanation as to the source of misconvergence in adaptive blind system identification.  View pdf