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Updated: Aug 7, 2025

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UWB Localization Based on Improved Robust Adaptive Cubature Kalman Filter.

Jiaqi Dong1, Zengzeng Lian1, Jingcheng Xu1

  • 1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an improved robust adaptive cubature Kalman filter (IRACKF) to enhance ultra-wideband (UWB) positioning accuracy. The new method effectively reduces errors from Non-Line-of-Sight observations and kinematic models.

Keywords:
NLOS signaladaptive filteringrobust filteringultra-wideband

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Area of Science:

  • Navigation Systems
  • Signal Processing
  • Estimation Theory

Background:

  • Ultra-wideband (UWB) systems face challenges with Non-Line-of-Sight (NLOS) observation errors and inaccurate kinematic models.
  • Existing robust and adaptive filtering techniques have different application conditions, potentially limiting positioning accuracy.

Purpose of the Study:

  • To propose an improved robust adaptive cubature Kalman filter (IRACKF) for UWB systems.
  • To address the limitations of current filtering methods by integrating robust and adaptive approaches with real-time error identification.

Main Methods:

  • Development of an improved robust adaptive cubature Kalman filter (IRACKF).
  • Implementation of a sliding window recognition scheme based on polynomial fitting for real-time error type identification.
  • Comparative analysis with robust CKF, adaptive CKF, and robust adaptive CKF algorithms.

Main Results:

  • The IRACKF algorithm demonstrated significant reductions in position error compared to existing methods.
  • Position errors were reduced by 38.0% compared to robust CKF, 45.1% compared to adaptive CKF, and 25.3% compared to robust adaptive CKF.
  • The proposed algorithm shows improved positioning accuracy and stability in UWB systems.

Conclusions:

  • The IRACKF algorithm effectively mitigates NLOS observation errors and kinematic model inaccuracies in UWB systems.
  • The integrated approach of robust adaptive filtering with real-time error identification enhances positioning performance.
  • The study confirms the superiority of IRACKF in improving the accuracy and stability of UWB positioning.