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ICASSP
2011
IEEE

Identification of ARMA models using intermittent and quantized output observations

11 years 3 months ago
Identification of ARMA models using intermittent and quantized output observations
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.
Damián Marelli, Keyou You, Minyue Fu
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Damián Marelli, Keyou You, Minyue Fu
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