Identification of ARMA models using intermittent and quantized output observations

This paper studies system identification of ARMA models whose outputs are subject to finite-level quantization and random packet dropouts. A simple adaptive quantizer and the corresponding recursive identification algorithm are proposed and shown to be optimal in the sense of asymptotically achieving the minimum mean square estimation error. The joint effects of finite-level quantization and random packet dropouts on identification accuracy are exactly quantified. The theoretic results are verified by simulations.