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A Combined UWB/IMU Localization Method with Improved CKF.

Pengfei Ji1, Zhongxing Duan1, Weisheng Xu1

  • 1College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710311, China.

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Summary
This summary is machine-generated.

This study introduces an improved Cubature Kalman Filter (CKF) for accurate ultra-wide band (UWB) localization, even with noisy sensor data. The novel method enhances precision in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments.

Keywords:
Levenberg–Marquardtcubature Kalman filternon-line-of-sightultra-wide band

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Area of Science:

  • Robotics and Navigation
  • Signal Processing
  • Sensor Fusion

Background:

  • Accurate localization is critical for autonomous systems.
  • Ultra-wide band (UWB) systems face challenges in noisy environments with unknown statistical properties.
  • Existing methods like Extended Kalman Filter (EKF), Unbiased Kalman Filter (UKF), and Cubature Kalman Filter (CKF) have limitations in complex scenarios.

Purpose of the Study:

  • To propose a novel combinatorial localization method for UWB systems.
  • To address inaccurate localization in environments with significant noise variations and unknown statistical properties.
  • To improve the robustness and accuracy of UWB localization, particularly in non-line-of-sight (NLOS) conditions.

Main Methods:

  • A combinatorial localization approach integrating UWB and Inertial Measurement Unit (IMU) data.
  • Utilizing the Levenberg-Marquardt (L-M) algorithm for optimal UWB position solving, overcoming least-squares initial value sensitivity.
  • Enhancing the traditional CKF algorithm with an adaptive factor for real-time measurement noise covariance updates and a fading factor to mitigate filter divergence.

Main Results:

  • Field experiments validated the proposed method in both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios.
  • The method demonstrated high localization accuracy across different environmental conditions.
  • Significant improvements in localization accuracy were observed in NLOS scenarios compared to EKF (25.2%), UKF (18.3%), and CKF (11.3%).

Conclusions:

  • The proposed combinatorial localization method effectively enhances UWB localization accuracy and robustness.
  • The integration of L-M optimization and adaptive CKF provides superior performance in challenging environments.
  • This approach offers a promising solution for precise UWB positioning in real-world applications, especially where NLOS conditions are prevalent.