Article ID Journal Published Year Pages File Type
695958 Automatica 2013 10 Pages PDF
Abstract

This paper studies 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 normal. We also propose a simple adaptive quantization scheme, which asymptotically achieves the minimum parameter estimation error covariance. The joint effect of finite-level quantization and random packet dropouts on identification accuracy are exactly quantified. The theoretical results are verified by simulations.

Related Topics
Physical Sciences and Engineering Engineering Control and Systems Engineering
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