Identification of ARMA models using intermittent and quantized output observations

Authors:
DamiáN Marelli;Keyou You;Minyue Fu
Affiliations:
School of Electrical Engineering and Computer Science, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia;Department of Automation, Tsinghua University, Beijing, 100084, PR China;School of Electrical Engineering and Computer Science, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia and Department of Control Science and Engineering, Zhejiang Univers ...
Venue:
Automatica (Journal of IFAC)
Year:
2013

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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.