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A Fusion Strategy for Vehicle Positioning at Intersections Utilizing UWB and Onboard Sensors.

Huaikun Gao1, Xu Li1, Xiang Song2

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

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Summary

This study presents a fusion strategy for reliable vehicle positioning using ultrawide bandwidth (UWB) and onboard sensors. The method effectively addresses non-line-of-sight (NLOS) issues in urban traffic, improving positioning accuracy.

Keywords:
AIMMARIMA–GARCHNLOSUWBonboard sensorsurban scenariosvehicle positioning

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Area of Science:

  • Intelligent Transportation Systems (ITS)
  • Wireless Positioning Technologies
  • Sensor Fusion

Background:

  • High-precision vehicle localization is crucial for Intelligent Transportation Systems (ITS).
  • Ultrawide bandwidth (UWB) offers high-precision positioning but suffers from non-line-of-sight (NLOS) propagation and multipath effects in urban traffic.
  • Existing UWB positioning methods require enhancement to ensure reliability in challenging traffic environments.

Purpose of the Study:

  • To develop a robust fusion strategy for reliable and precise vehicle positioning by integrating UWB and onboard sensor data.
  • To address and mitigate the impact of non-line-of-sight (NLOS) propagation and other interferences on UWB positioning in vehicular traffic scenarios.
  • To enhance the accuracy and adaptability of vehicle positioning systems for dynamic driving conditions.

Main Methods:

  • A two-step approach involving UWB raw measurement preprocessing and global position estimation.
  • Development of an ARIMA-GARCH model for efficient detection and correction of UWB non-line-of-sight (NLOS) errors.
  • Implementation of an adaptive interacting multiple model (AIMM) algorithm for global sensor fusion, outperforming traditional IMM by adapting model probabilities to current driving conditions.

Main Results:

  • The proposed ARIMA-GARCH model effectively detects and corrects UWB NLOS errors in vehicular traffic.
  • The adaptive IMM (AIMM) algorithm demonstrates improved positioning accuracy by dynamically adjusting model probabilities.
  • Experimental validation confirms the effectiveness and feasibility of the UWB and onboard sensor fusion strategy.

Conclusions:

  • The developed fusion strategy provides a reliable and precise solution for vehicle positioning in Intelligent Transportation Systems (ITS).
  • The integration of UWB with onboard sensors, coupled with advanced error correction and adaptive fusion techniques, significantly enhances localization performance.
  • The proposed method offers a practical and effective approach for achieving lane-level or higher precision localization in complex urban environments.