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Automatic modeling algorithm of stochastic error for inertial sensors

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Abstract

This paper proposes an automatic algorithm to determine the properties of stochastic processes and their parameters for inertial error. The proposed approach is based on a recently developed method called the generalized method of wavelet moments (GMWM), whose estimator was proven to be consistent and asymptotically normally distributed. This algorithm is suitable mainly (but not only) for the combination of several stochastic processes, where the model identification and parameter estimation are quite difficult for the traditional methods, such as the Allan variance and the power spectral density analysis. This algorithm further explores the complete stochastic error models and the candidate model ranking criterion to realize automatic model identification and determination. The best model is selected by making the trade-off between the model accuracy and the model complexity. The validation of this approach is verified by practical examples of model selection for MEMS-IMUs (micro-electro-mechanical system inertial measurement units) in varying dynamic conditions.

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Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    Luodi Zhao & Long Zhao

  2. Digital Navigation Center, Beihang University, Beijing, 100191, China

    Luodi Zhao & Long Zhao

  3. Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing, 100191, China

    Luodi Zhao & Long Zhao

Authors
  1. Luodi Zhao
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  2. Long Zhao
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Correspondence to Long Zhao.

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The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

This work was supported by the National Science Foundation of China (Nos. 42274037, 41874034), the Beijing Natural Science Foundation (No. 4202041), and the National Key Research and Development Program of China (No. 2020YFB0505804).

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Zhao, L., Zhao, L. Automatic modeling algorithm of stochastic error for inertial sensors. Control Theory Technol. 22, 81–91 (2024). https://doi.org/10.1007/s11768-023-00183-6

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