  The task of inertial sensor calibration has required the development of
various techniques to take into account the sources of measurement error coming
from such devices. The calibration of the stochastic errors of these sensors
has been the focus of increasing amount of research in which the method of
reference has been the so-called "Allan variance slope method" which, in
addition to not having appropriate statistical properties, requires a
subjective input which makes it prone to mistakes. To overcome this, recent
research has started proposing "automatic" approaches where the parameters of
the probabilistic models underlying the error signals are estimated by matching
functions of the Allan variance or Wavelet Variance with their model-implied
counterparts. However, given the increased use of such techniques, there has
been no study or clear direction for practitioners on which approach is optimal
for the purpose of sensor calibration. This paper formally defines the class of
estimators based on this technique and puts forward theoretical and applied
results that, comparing with estimators in this class, suggest the use of the
Generalized Method of Wavelet Moments as an optimal choice.
