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Improved strong tracking Sage-Husa adaptive algorithm for multi-MEMS IMU data fusion.

Kunpeng Li1, Kaixuan Wang1, Sujing Song1

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Summary
This summary is machine-generated.

This study introduces an improved multi-IMU data fusion method using a strong tracking Sage-Husa adaptive Kalman filter (ST-SHAKF) for low-cost, high-precision inertial measurement. The novel approach enhances noise suppression and fusion accuracy compared to traditional methods.

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Area of Science:

  • * Instrumentation and Measurement
  • * Sensor Fusion
  • * Kalman Filtering

Background:

  • * Micro-electro-mechanical system inertial measurement units (IMUs) are crucial for inertial measurement.
  • * Traditional Sage-Husa adaptive Kalman filter (SHAKF) methods face challenges with parameterization and filter divergence.
  • * Accurate data fusion from multiple IMUs is essential for high-precision applications.

Purpose of the Study:

  • * To develop a low-cost, high-precision inertial measurement system using an array of 16 IMUs.
  • * To propose an improved multi-IMU data fusion method based on the strong tracking Sage-Husa adaptive Kalman filter (ST-SHAKF).
  • * To enhance the performance of the SHAKF algorithm for robust inertial measurement.

Main Methods:

  • * Development of a 16-IMU circuit array.
  • * Implementation of a simplified SHAKF with improved measurement noise variance estimation.
  • * Integration of a strong tracking filter to prevent filter divergence.
  • * Dynamic weight allocation using minimum variance estimation for multi-IMU fusion.

Main Results:

  • * The ST-SHAKF method demonstrated superior performance over the traditional SHAKF, evidenced by improved Allan variance and standard deviation.
  • * The proposed method achieved better noise suppression for both acceleration and angular velocity measurements.
  • * Enhanced fusion accuracy was observed under both static and dynamic experimental conditions.

Conclusions:

  • * The developed ST-SHAKF method offers a cost-effective solution for high-precision inertial measurement.
  • * The enhanced filter design effectively addresses SHAKF limitations, ensuring convergence and accuracy.
  • * The multi-IMU fusion technique provides significant improvements in inertial sensing capabilities.