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Updated: Jan 20, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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Optimized Multi-Position Calibration Method with Nonlinear Scale Factor for Inertial Measurement Units.

Zihui Wang1,2, Xianghong Cheng3,4, Jinbo Fu1

  • 1School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.

Sensors (Basel, Switzerland)
|August 25, 2019
PubMed
Summary

This study introduces an improved multi-position calibration method for inertial measurement units (IMUs), accounting for nonlinear scale factors. This method enhances navigation system (INS) accuracy by up to 17% in lab tests and 12% in real-world vehicle experiments.

Keywords:
inertial measurement unitsmulti-position methodnonlinear scale factoroptimization method

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Area of Science:

  • Navigation Systems Engineering
  • Sensor Calibration Techniques
  • Inertial Measurement Unit (IMU) Technology

Background:

  • Inertial Navigation Systems (INS) require regular calibration of Inertial Measurement Units (IMUs) for optimal performance.
  • Existing multi-position calibration methods for IMUs often rely on simplified linear scale factor models.
  • Accurate calibration, including nonlinear scale factors, is crucial for improving INS precision.

Purpose of the Study:

  • To propose an optimized multi-position calibration method for IMUs that incorporates nonlinear scale factors.
  • To design an optimal calibration motion for IMUs based on cost function sensitivity analysis.
  • To enhance the accuracy and robustness of iterative calibration methods through an improved initial value estimation technique.

Main Methods:

  • Developed an optimized multi-position calibration method addressing nonlinear scale factors in IMUs.
  • Designed an optimal IMU calibration motion by analyzing the sensitivity of the cost function to calibration parameters.
  • Implemented an estimation method for initial values to improve the convergence and reliability of iterative optimization algorithms.

Main Results:

  • The proposed method, incorporating nonlinear scale factors, demonstrated superior performance compared to methods using only linear scale factors.
  • Simulations and experimental results confirmed the effectiveness of the optimized calibration approach.
  • Navigation accuracy improvements of up to 17% in laboratory settings and 12% during moving vehicle experiments were achieved.

Conclusions:

  • The optimized multi-position calibration method effectively calibrates IMUs with nonlinear scale factors, leading to enhanced INS performance.
  • The proposed method offers a practical solution for improving the precision and reliability of navigation systems.
  • Accurate calibration of nonlinear scale factors is essential for achieving state-of-the-art navigation accuracy.