Identification of ARMA Models using Intermittent and Quantized Output Observations
In this talk, we study system identification of ARMA models whose outputs are subject to finite-level quantization and random packet dropouts. Using the maximum likelihood criterion, we propose a recursive identification algorithm, which we show to be strongly consistent and asymptotically efficient. We also propose a simple adaptive quantization scheme, which asymptotically achieves the minimum parameter estimation error covariance. The joint effects of finite-level quantization and random packet dropouts on identification accuracy are quantified. The theoretic results are verified by simulations.