Identification of ARMA models using intermittent and quantized output observations

Title: Identification of ARMA models using intermittent and quantized output observations
Creator: Marelli, Damián; You, Keyou; Fu, Minyue
Relation: Automatica Vol. 49, Issue 2, p. 360-369
Publisher Link: http://dx.doi.org/10.1016/j.automatica.2012.11.020
Publisher: Elsevier BV
Resource Type: journal article
Date: 2013
Description: 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.
Subject: identification methods; network-based computing systems; ARMA model; finite-level quantization; packet dropout
Identifier: http://hdl.handle.net/1959.13/1295313
Identifier: uon:18996
Identifier: ISSN:0005-1098
Language: eng
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