An Algorithm for Automatic Inertial Sensors Calibration

S. Guerrier, R. Molinari, J. Skaloud, M-P. Victoria-Feser

Abstract:	The integration of observations issued from a satellite-based system (GNSS) with an Inertial Navigation System (INS) is usually performed through a Bayesian filter such as the Extended Kalman Filter (EKF). The task of designing the navigation EKF is strongly related to the inertial sensor error modeling problem. Accelerometers and gyroscopes may be corrupted by random errors of complex spectral structure. Consequently, identifying correct error-state parameters in the INS/GNSS EKF becomes difficult when several stochastic processes are superposed. In such situations, classical approaches like the Allan variance or PSD analysis fail due to the difficulty of separating the error-processes in the spectral domain. In this context, a suitable alternative is provided by the recently developed method called the Generalized Method of Wavelet Moments (GMWM) which was proven to be consistent and asymptotically normally distributed. The principle of this estimation method is to match the theoretical and sample-based wavelet variances (WV). This article proposes a goodness-of-fit criterion which can be used to determine the suitability of a candidate error model for inertial sensors. This model selection approach relies on an unbiased estimate of the distance between the theoretical WV and the empirical WV which would be obtained on an independent sample issued from the process of interest. In some sense, the proposed methodology is a generalization of Mallow's Cp applied to models estimated by the GMWM. By allowing to rank candidate models, this approach permits to construct an algorithm for automatic sensor calibration. The benefits of this methodology are highlighted by providing practical examples of model selection for MEMS-IMUs of higher quality. Particularly, we investigate the time series issued from eight IMUs of the same manufacturer and determine a common model to describe the random behavior of these sensors. We also analyze the quality of the determined trajectory provided by the INS/GNSS Kalman filter, in which artificial GNSS gaps were introduced. It has been demonstrated that such an approach significantly improves in the trajectory accuracy.
Published in:	Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013) September 16 - 20, 2013 Nashville Convention Center, Nashville, Tennessee Nashville, TN
Pages:	1092 - 1097
Cite this article:	Guerrier, S., Molinari, R., Skaloud, J., Victoria-Feser, M-P., "An Algorithm for Automatic Inertial Sensors Calibration," Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013), Nashville, TN, September 2013, pp. 1092-1097.
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