Scientific article
Open access

A computationally efficient framework for automatic inertial sensor calibration

Published inIEEE sensors journal, vol. 18, no. 4, p. 1636-1646
Publication date2018

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.

  • Generalized method of wavelet moment
  • Wavelet variance
  • Allan variance
  • Error modeling
  • Time serie
  • Estimation method
  • Micro-electro-mechanical system
  • Kalman filter
  • R software
  • arxiv : stat.AP
Citation (ISO format)
BALAMUTA, James et al. A computationally efficient framework for automatic inertial sensor calibration. In: IEEE sensors journal, 2018, vol. 18, n° 4, p. 1636–1646. doi: 10.1109/JSEN.2017.2773663
Main files (1)
Article (Submitted version)
ISSN of the journal1530-437X

Technical informations

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