  Sensors in high-precision mechatronic systems require accurate calibration,
which is achieved using test beds that, in turn, require even more accurate
calibration. The aim of this paper is to develop a cascaded calibration method
for position sensors of mechatronic systems while taking into account the
variance of the calibration model of the test bed. The developed calibration
method employs Gaussian Process regression to obtain a model of the
position-dependent sensor inaccuracies by combining prior knowledge of the
sensor with data using Bayesian inference. Monte Carlo simulations show that
the developed calibration approach leads to significantly higher calibration
accuracy when compared to alternative regression techniques, especially when
the number of available calibration points is limited. The results indicate
that more accurate calibration of position sensors is possible with fewer
resources.
