Inertial sensor calibration plays a progressively

important role in many areas of research among which navigation

engineering. By performing this task accurately, it is possible

to significantly increase general navigation performance by cor-

rectly filtering out the deterministic and stochastic measurement

errors that characterize such devices. While different techniques

are available to model and remove the deterministic errors, there

has been considerable research over the past years with respect

to modelling the stochastic errors which have complex structures.

In order to do the latter, different replicates of these error signals

are collected and a model is identified and estimated based on one

of these replicates. While this procedure has allowed to improve

navigation performance, it has not yet taken advantage of the

information coming from all the other replicates collected on the

same sensor. However, it has been observed that there is often

a change of error behaviour between replicates which can also

be explained by different (constant) external conditions under

which each replicate was taken. Whatever the reason for the

difference between replicates, it appears that the model structure

remains the same between replicates but the parameter values

vary. In this work we therefore consider and study the properties

of different approaches that allow to combine the information

from all replicates considering this phenomenon, confirming their

validity both in simulation settings and also when applied to real

inertial sensor error signals. By taking into account parameter

variation between replicates, this work highlights how these

approaches can improve the average navigation precision as well

as obtain reliable estimates of the uncertainty of the navigation

solution