A novel finite-time convergent estimation technique is prop
osed for identifying the amplitude, frequency and phase of a
biased
sinusoidal signal. Resorting to Volterra integral operato
rs with suitably designed kernels, the measured signal is pr
ocessed
yielding a set of auxiliary signals in which the influence of t
he unknown initial conditions is removed. A second-order sl
iding
mode-based adaptation law – fed by the aforementioned auxil
iary signals – is designed for finite-time estimation of the f
requency,
amplitude, and phase. The worst case behavior of the propose
d algorithm in presence of the bounded additive disturbance
s
is fully characterized by Input-to-State Stability argume
nts. The effectiveness of the estimation technique is evalua
ted and
compared with other existing tools via extensive numerical
simulations.