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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/930309

Title

Identification of ARMA models using intermittent and quantized output observations

Author/Creator

Marelli, Damián;  You, Keyou;  Fu, Minyue

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.

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

Date

2011

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Keyword(s)

accuracy;  joints;  kalman filters;  maximum likelihood estimation;  quantization;  wireless sensor networks

Resource Type

conference paper

Identifier

http://hdl.handle.net/1959.13/930309

Identifier

ISSN:1520-6149

Language

eng

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Subject

"Identification of ARMA models using intermittent and quantized output observations"

 

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