A blind identification algorithm robust to order over estimation

Active research in blind identification of single input multiple output (SIMO) channels has led to a variety of second order statistics based algorithms, mainly the subspace and the linear prediction approaches. The subspace algorithm shows good performance, although it requires exact knowledge of the channel order, which is not guaranteed by current order detection algorithms. The linear prediction algorithm is sensitive to observation noise while its robustness to channel order over estimation is only theoretical. We propose a new second order statistics based blind channel identification algorithm using a shifted version of the channel output covariance matrix. It proves to be truly robust to channel order over estimation i.e., able to estimate the channel impulse response when the assumed channel order is greater than the exact order and when channel output is corrupted by additive noise and observed over finite time duration. Moreover, the proposed algorithm shows clearly better performance than the linear prediction algorithm.