Deadbeat Kernel-based Frequency Estimation of a Biased Sinusoidal Signal

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.