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Kernel-based Deadbeat Parametric Estimation of Bias-affected Damped Sinusoidal Signals

Li, P.
•
Fedele, G.
•
Pin, G.
•
PARISINI, Thomas
2016
  • conference object

Abstract
This paper deals with a novel non-asymptotic algorithm to estimate four characteristic parameters of biased damped sinusoidal signals. The proposed scheme is based on the linear integral technique introduced by [1], which relies on processing the measured signal by Volterra operators with suitably designed kernel functions. The main feature of the proposed kernels consists in the possibility to annihilate the effect of the unknown initial conditions of the hidden internal states of the system. Therefore, in the ideal case, finite-time convergence of the estimation error can be obtained. Extensive numerical simulations are presented confirming the effectiveness and the robustness of the proposed methodology.
WOS
WOS:000392695300084
Archivio
http://hdl.handle.net/11368/2879121
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85015095467
Diritti
metadata only access
Soggetti
  • Sinusoidal estimation...

Visualizzazioni
1
Data di acquisizione
Apr 19, 2024
Vedi dettagli
google-scholar
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