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unige:96033 
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A computationally efficient framework for automatic inertial sensor calibration |
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| 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 moment — Wavelet variance — Allan variance — Error modeling — Time serie — Estimation method — Micro-electro-mechanical system — Kalman filter — R software | |
| Identifiers | arXiv: 1603.05297v2 | |
| Note | 20 pages, 6 figures | |
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| Citation (ISO format) | 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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