The task of inertial sensor calibration has become increasingly important due to the growing use of low-cost inertial measurement units which are however characterized by measurement errors. Being widely employed in a variety of mass-market applications, there is considerable focus on compensating for these errors by taking into account the deterministic and stochastic factors that characterize them. In this paper, we focus on the stochastic part of the error signal where it is customary to record the latter and use the observed error signal to identify and estimate the stochastic models, often complex in nature, that underlie this process. However, it is often the case that these error signals are observed through a series of replicates for the same inertial sensor and equally often that these replicates have the same model structure but their parameters appear different between replicates. This phenomenon has not been taken into account by current stochastic calibration procedures which therefore can be conditioned by flawed parameter estimation. For this reason, this paper aims at studying different approaches for this problem and studying their properties to take into account parameter variation between replicates thereby improving measurement precision and navigation uncertainty quantification in the long run.
The code for the R package (under development) can be found here.