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Multiple IMU sensor fusion using resolution refinement method to reduce quantization error

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Abstract

We propose a sensor fusion method of multiple inertial measurement units (IMU) with different resolutions to reduce quantization errors and improve the measurement accuracy of dead reckoning navigation. The resolution and dynamic range of the accelerometer within MEMS IMU have a trade-off relationship; the selection of a fixed dynamic range in a single IMU may increase the error of the inertial navigation system. We integrate multiple accelerometer signals with different resolutions into a weighted average that depends on the measurements. This reduces the quantization error by creating an optimal resolution tailored to the specific measurement range. To verify the proposed algorithm, we examined the effect of mathematically generated accelerometer values.

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Abbreviations

RRM :

Resolution refinement method

IMU :

Inertial measurement unit

MEMS :

Micro-electro-mechanical systems

H(s) :

Sigmoid function

d avg :

Average of the distances between the exact trajectory and estimated trajectory

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Acknowledgments

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21AMDP-C162388-01).

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Authors and Affiliations

  1. Department of Automobile System Engineering, Joongbu University, Goyang, Gyeonggi-do, Korea

    Sung Sic Yoo & Heung-Shik Lee

  2. Department of Electrical and Computer Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon, 22212, Korea

    Humayun Kabir

Authors
  1. Sung Sic Yoo
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  2. Humayun Kabir
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  3. Heung-Shik Lee
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Corresponding author

Correspondence to Heung-Shik Lee.

Additional information

Sung Sic Yoo is currently A Research Professor in the Department of Automotive Systems Engineering at Joongbu University, and is interested in sensor fusion, smart mobility technology, numerical analysis.

Humayun Kabir is currently an integrated Ph.D. student majoring in Future Vehicle Engineering at the Department of Electrical and Computer Engineering, Inha University, South Korea. His research interests include machine learning, deep learning, sensor fusion and robotics.

Heung-Shik Lee is currently an Assistant Professor of Automotive Systems Engineering at Joongbu University, and is interested in sensor control, smart mobility technology, and intelligent structures using nanomaterial structures.

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Yoo, S.S., Kabir, H. & Lee, HS. Multiple IMU sensor fusion using resolution refinement method to reduce quantization error. J Mech Sci Technol 37, 163–168 (2023). https://doi.org/10.1007/s12206-022-1216-1

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  • DOI: https://doi.org/10.1007/s12206-022-1216-1

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