Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.automatica.2012.11.020 | Automatica (Journal of IFAC) |
Keywords | DocType | Volume |
Identification methods,Network-based computing systems,ARMA model,Finite-level quantization,Packet dropout | Journal | 49 |
Issue | ISSN | Citations |
2 | 0005-1098 | 19 |
PageRank | References | Authors |
0.95 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Damián Marelli | 1 | 164 | 19.58 |
| Keyou You | 2 | 831 | 50.16 |
| Minyue Fu | 3 | 1878 | 221.17 |