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Related Experiment Video

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An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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A Robust and Adaptive Complementary Kalman Filter Based on Mahalanobis Distance for Ultra Wideband/Inertial

Xin Li1,2, Yan Wang3, Kourosh Khoshelham4

  • 1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. linuxcumt@126.com.

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Summary

This study introduces a robust Ultra-Wideband/Inertial Measurement Unit (UWB/IMU) fusion filter to improve indoor positioning accuracy. The filter effectively detects and mitigates outliers from non-line-of-sight conditions, enhancing reliability.

Keywords:
Mahalanobis distanceUWB/IMU fusionadaptively-robust filtercomplementary Kalman filter

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Area of Science:

  • Robotics and Navigation
  • Sensor Fusion
  • Signal Processing

Background:

  • Ultra-wideband (UWB) offers high accuracy for indoor positioning but is susceptible to outliers under non-line-of-sight (NLOS) conditions.
  • Existing methods struggle to reliably filter erroneous UWB measurements, impacting positioning performance in real-world scenarios.
  • Integration with Inertial Measurement Units (IMUs) is common, but robust fusion under adverse conditions remains a challenge.

Purpose of the Study:

  • To develop and validate a novel UWB/IMU fusion filter designed to detect and eliminate outlying UWB observations.
  • To enhance the robustness and accuracy of indoor positioning systems operating in challenging NLOS environments.
  • To improve the reliability of UWB-based localization by addressing data anomalies and system state exceptions.

Main Methods:

  • A Complementary Kalman Filter framework is employed for tracking position, velocity, and direction errors.
  • The least squares method is utilized to calculate positioning residuals and dynamically set observation weights based on a robustness factor.
  • Mahalanobis distance is used for detecting system state abnormalities and mitigating the impact of outlying UWB data through robust filtering.

Main Results:

  • The proposed filter effectively estimates observation errors, distinguishing between trusted and abnormal UWB measurements.
  • Adaptive filtering is applied for normal observations, while robust filtering minimizes the impact of outliers when the robustness factor exceeds a threshold.
  • Experimental results demonstrate improved positioning accuracy despite varying quantities of system state outliers and proportions of outlying observations.

Conclusions:

  • The UWB/IMU fusion filter provides a robust solution for accurate indoor positioning in the presence of UWB outliers.
  • The dynamic weighting and robust filtering mechanisms effectively handle non-line-of-sight (NLOS) conditions and data anomalies.
  • This approach significantly enhances the reliability and performance of UWB-based localization systems.