Evaluation of adaptive blind SIMO identification in terms of a normalized filter-projection misalignment

The blind identification of single-input multiple-output (SIMO) systems suffers in the presence of near-common and exact common zeros between the channels, particularly in conjunction with observation noise. In general, we notice an ambiguity of the identification which cannot be resolved without further a priori information on the channel coefficients. In order to enable an adequate evaluation of blind SIMO identification in such cases, we develop the normalized filter-projection misalignment (NFPM), which represents a multichannel squared-error distance between true and estimated channels, while absorbing a common filter error due to a possible lack of identifiability. Using the NFPM measure, we demonstrate experimentally that the steady-state performance of the blind multichannel least mean-square (MCLMS) algorithm in the presence of missing channel diversity and noise is in line with the results obtained from supervised least mean-square (LMS) system identification.