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UWB/MEMS IMU integrated positioning method based on NLOS angle discrimination and MAP constraints.

Xin Sui1, Jiapeng Song2, Changqiang Wang1

  • 1School of Geomatics, Liaoning Technical University, Fuxin, 123000, China.

Scientific Reports
|August 27, 2024
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Summary

This study introduces a dynamic non-line-of-sight (NLOS) angle discrimination method for ultrawideband (UWB) signals. The novel algorithm improves UWB/MEMS IMU positioning accuracy in dynamic, occluded environments.

Keywords:
Dynamic NLOS angle discriminationFast line segment matchingMap databaseRobust Kalman filterUWB/MEMS IMU integration

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Area of Science:

  • Navigation and Positioning Systems
  • Signal Processing
  • Robotics and Autonomous Systems

Background:

  • Ultrawideband (UWB) signals face localization challenges in dynamic environments due to non-line-of-sight (NLOS) conditions and signal occlusion.
  • Existing NLOS error characterization for UWB signals in dynamic settings is insufficient, hindering reliable positioning.
  • Signal occlusion significantly impacts UWB ranging accuracy, especially at specific horizontal angles.

Purpose of the Study:

  • To propose a dynamic NLOS angle discrimination method for UWB signals to enhance localization accuracy.
  • To develop a tightly integrated UWB/MEMS IMU positioning algorithm that accounts for dynamic NLOS characteristics.
  • To improve the robustness of UWB positioning in environments with signal occlusion and multipath effects.

Main Methods:

  • A dynamic NLOS angle discrimination technique based on horizontal angles was developed to identify and weight UWB ranging observations.
  • A tightly integrated positioning algorithm combining UWB and MEMS IMU data was implemented, incorporating NLOS angle discrimination and map constraints.
  • UWB signal occlusion characteristics were analyzed concerning horizontal angles to understand ranging error behavior.

Main Results:

  • The proposed algorithm achieved low positioning errors: 0.189m (northward), 0.126m (eastward), and 0.243m (planar) in initial experiments.
  • Further experiments yielded improved errors: 0.119m (northward), 0.134m (eastward), and 0.211m (planar).
  • Positional accuracy in the plane showed significant improvements of 22.9% and 28.5% compared to the Robust Adaptive Kalman Filtering algorithm.

Conclusions:

  • The dynamic NLOS angle discrimination method effectively mitigates UWB ranging errors in occluded environments.
  • The integrated UWB/MEMS IMU algorithm demonstrates superior positioning performance compared to traditional methods, especially in challenging dynamic scenarios.
  • The findings contribute to more reliable and accurate localization systems utilizing UWB technology in real-world applications.