A State-Space Cross-Relation Approach to Adaptive Blind SIMO System Identification

In this work, we address blind single-input multiple-output (SIMO) system identification in conjunction with dynamical modeling of the underlying system. A multichannel cross-relation observation model in the DFT domain is employed to derive a blind adaptive algorithm that recursively learns the posterior distribution on the unknown SIMO system. The proposed algorithm inherently incorporates the time-varying nature of the channels and a representation of the observation noise. We show that the resulting cross-relation state-space frequency-domain adaptive filter (CR-SSFDAF), owing to its stable and diagonalized structure and near-optimal step-size control, can be efficiently operated in time-varying and noisy conditions.