Paper Info
Title
Deadbeat kernel-based frequency estimation of a biased sinusoidal signal
Abstract
This paper introduces a novel deadbeat frequency estimator for possibly biased noisy sinusoidal signals. The proposed estimation scheme is based on processing the measurements by Volterra integral operators with suitably designed kernels, that allow to obtain auxiliary signals not affected by the unknown initial conditions. These auxiliary signals are exploited to adapt the frequency estimate with a variable structure adaptation law that yields finite-time convergence of the estimation error. The worst case behavior of the proposed algorithm in the presence of bounded additive disturbances is characterized by Input-to-State Stability arguments. Numerical simulations are given to show the effectiveness of the proposed method and to compare it with some other techniques available in the recent literature.
Year
DOI
Venue
2015
10.1109/ECC.2015.7330589
2015 European Control Conference (ECC)
Keywords
Field
DocType
deadbeat kernel-based frequency estimation,biased sinusoidal signal,deadbeat frequency estimator,Volterra integral operators,auxiliary signals,variable structure adaptation law,estimation error finite-time convergence,bounded additive disturbances,input-to-state stability arguments
Kernel (linear algebra),Convergence (routing),Algorithm design,Noise measurement,Control theory,Variable kernel density estimation,Numerical stability,Mathematics,Bounded function,Estimator
Conference
Citations 
PageRank 
References 
0
0.34
19
Authors
3
Name
Order
Citations
PageRank
Gilberto Pin113617.21
Boli Chen2316.94
T Parisini3935113.17