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The blind identification of single-input multiple-output (SIMO) systems is often performed by exploiting a cross-relation (CR) between channel pairs. It has been shown that this property allows an estimation of channel impulse responses up to a common gain factor if certain identifiability conditions are met. In this case, the estimated channels can be evaluated by a gain-compensated system distance known as normalized projection misalignment (NPM). Current algorithms for blind channel identification, however, suffer in the presence of insufficient channel diversity and observation noise. In this paper, we first demonstrate that in the absence of noise the CR identification error for channels with exact common zeros is given by a single-channel pole-zero transfer function. Next, we extend our analysis to the realistic case of near-common zeros and noise for which we show that the effective error can still be approximated by a common transfer function as long as the distance between the channel zeros remains below a signal-to-noise ratio-dependent threshold. A finite impulse response (FIR) modeling of the error then enables us to define a common-filter-error-compensated system distance, termed normalized filter-projection misalignment (NFPM), which establishes a natural extension to the NPM analysis. By finally considering realistic channels, which we blindly estimate with the adaptive multichannel least mean-square (MCLMS) algorithm, we demonstrate that the NFPM reliably reaches the noise floor, confirming that the effective error can be approximated by a common FIR filter.