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An Efficient IAKF Approach for Indoor Positioning Drift Correction.

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Summary

This study introduces an improved adaptive Kalman filter (IAKF) to correct indoor positioning errors caused by people interference. The new algorithm significantly reduces localization drift in ultra-wideband systems.

Keywords:
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Area of Science:

  • Robotics and Automation
  • Signal Processing
  • Wireless Communication

Background:

  • Indoor Positioning Systems (IPS) commonly use ultra-wideband (UWB) technology.
  • Triangulation algorithms in IPS are susceptible to localization drift errors due to environmental factors and network topology.
  • People interference is a significant challenge affecting real-time positioning accuracy.

Purpose of the Study:

  • To develop a novel indoor positioning shift correction architecture.
  • To address and mitigate localization drift errors in UWB-based IPS, particularly under people interference conditions.
  • To enhance the accuracy of real-time positioning points.

Main Methods:

  • Development of an improved adaptive Kalman filter (IAKF) algorithm.
  • Simulation and analysis of four localization drift error correction algorithms: Movement Average (MA), Least Square (LS), Kalman Filter (KF), and IAKF.
  • Implementation and verification of the IAKF algorithm on a UWB indoor positioning system.

Main Results:

  • The IAKF algorithm demonstrated significant improvements in reducing drift errors.
  • Drift errors were reduced by 60% in environments with surrounding crowds.
  • A 74.15% improvement in drift error was observed in environments without crowds.
  • Real-time positioning points achieved greater proximity to actual target locations.

Conclusions:

  • The developed IAKF algorithm effectively corrects localization drift errors in UWB IPS.
  • The IAKF algorithm offers superior performance compared to MA, LS, and KF algorithms, especially in crowded environments.
  • This architecture enhances the reliability and accuracy of indoor positioning solutions.