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Title

A computationally efficient framework for automatic inertial sensor calibration

Authors
Balamuta, James
Guerrier, Stephane
Yang, Wenchao
Published in IEEE Sensors Journal. 2016
Abstract The calibration of (low-cost) inertial sensors has become increasingly important over the past years since their use has grown exponentially in many applications going from unmanned aerial vehicle navigation to 3D-animation. However, this calibration procedure is often quite problematic since the signals issued from these sensors have a complex spectral structure and the methods available to estimate the parameters of these models are either unstable, computationally intensive and/or statistically inconsistent. This paper presents a new software platform for inertial sensor calibration based on the Generalized Method of Wavelet Moments which provides a computationally efficient, flexible, user-friendly and statistically sound tool to estimate and select from a wide range of complex models. The software is developed within the open-source statistical software R and is based on C++ language allowing it to achieve high computational performance.
Keywords Generalized method of wavelet momentWavelet varianceAllan varianceError modelingTime serieEstimation methodMicro-electro-mechanical systemKalman filterR software
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Note 20 pages, 6 figures
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BALAMUTA, James et al. A computationally efficient framework for automatic inertial sensor calibration. In: IEEE Sensors Journal, 2016. https://archive-ouverte.unige.ch/unige:96033

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Deposited on : 2017-08-07

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