The problem of identification of linear quasi-periodically varying systems is considered. This problem can be solved using generalized adaptive notch filtering (GANF) algorithms. It is shown that accuracy of system parameter estimation can be increased if the results obtained from GANF are further processed using a cascade of appropriately designed filters. The resulting generalized adaptive notch smoothing (GANS) algorithms can be employed in off-line applications where causality constraints do not apply. When the instantaneous frequency of parameter changes varies in a sufficiently smooth manner, the proposed GANS algorithm, based on a new, quasi-linear model of frequency drift, outperforms the existing solutions.
Autorzy
Informacje dodatkowe
- Kategoria
- Aktywność konferencyjna
- Typ
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Język
- angielski
- Rok wydania
- 2011
