In this work we address the problem of rigorously

evaluating the performances of a inertial navigation system under

design in presence of multiple alternative choices. We introduce

a framework based on Monte-Carlo simulations in which a

standard extended Kalman filter is coupled with realistic and

user-configurable noise generation mechanisms and attempts to

recover a reference trajectory from noisy measurements. The

evaluation of several statistical metrics of the solution, aggregated

over hundreds of realizations, gives a reasonable estimate of the

expected performances of the system in real-world conditions and

allow the user to operate the choice between alternative setups.

To show the generality of our approach, we consider an example

application to the problem of stochastic calibration. Two compet-

ing stochastic modeling techniques, namely, the widely popular

Allan variance linear regression, and the emerging generalised

method of wavelet moments are rigorously compared in terms

of the framework defined metrics and in multiple scenarios. We

find that the latter provides substantial advantages and should

be preferred, at least for certain classes of inertial sensors. Our

framework allows to consider a wide range of problems related

to the quantification of navigation system performances such as,

for example, the robustness of an INS with respect to outliers or

other modeling imperfections. While real world experiments are

essential to assess to performance of new methods they tend to be

costly and are typically unable to lead to a sufficient number of

replicates to evaluate, for example, the correctness of estimated

uncertainty. Therefore, our method can bridge the gap between

these experiments and pure statistical consideration as done, for

example, in the stochastic calibration literature