Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
695958 | Automatica | 2013 | 10 Pages |
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
Authors
Damián Marelli, Keyou You, Minyue Fu,