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: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011). Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011) (Prague 22-27 May, 2011) p. 4076-4079
Publisher Link: http://dx.doi.org/10.1109/ICASSP.2011.5947248
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Resource Type: conference paper
Date: 2011
Description: This paper studies system identification of ARMA models whose outputs are subject to finite-level quantization and random packet dropouts. A simple adaptive quantizer and the corresponding recursive identification algorithm are proposed and shown to be optimal in the sense of asymptotically achieving the minimum mean square estimation error. The joint effects of finite-level quantization and random packet dropouts on identification accuracy are exactly quantified. The theoretic results are verified by simulations.
Subject: accuracy; joints; kalman filters; maximum likelihood estimation; quantization; wireless sensor networks
Identifier: http://hdl.handle.net/1959.13/930309
Identifier: uon:10815
Identifier: ISSN:1520-6149
Language: eng
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