Automatic and computationally efficient method for model selection in inertial sensor calibration

The identification and selection of a small set of models that are able to well describe and predict the error signals coming from IMU sensors is of utmost importance to improve the navigation precision of these devices. For this reason, in this paper we propose a new model selection criterion that has specific improvements on existing criteria compared to which it is able to estimate with a greater computational efficiency. These criteria are based on the Generalized Method of Wavelet Moments that was recently proposed to estimate the parameters of IMU error models. Using this approach, the new model selection procedure is included within an algorithm that allows it to be executed more efficiently and is implemented within a new package in the open-source statistical platform R. Simulation studies and applied examples show the advantages of this new model selection procedure which enables engineers and researchers to identify a restricted set of models for IMU sensor calibration.