An inertial sensor calibration platform to estimate and select error models

A new open-source software platform that, among others, allows to select models for inertial sensor stochastic calibration is presented in this paper. This platform consists in a package included in the statistical software R. The identification of stochastic models and estimation of model parameters is based on the method of Generalized Method of Wavelet Moments. This approach provides an extremely general framework for the identification, estimation and testing of models to describe and predict the error signals coming from inertial sensors. With the possibility of estimating complex models made of the sum of different underlying processes, this paper also presents the method with which a model, or a restrict set of models, can be selected that best describes and predicts the error signal.