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Updated: Jul 2, 2025

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Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment.

Rahul Ranjan1, Donggyu Shin2, Yoonsik Jung2

  • 1Department of Computer and Electronic Convergence, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of Korea.

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Summary
This summary is machine-generated.

This study enhances ultra-wideband (UWB) localization by integrating filters. The extended Kalman filter with a low-pass filter (EKF + LPF) significantly improves indoor positioning accuracy and reduces noise.

Keywords:
Kalman filter (KF)LiDARextended Kalman filter (EKF)moving average filter (MVG)robot navigationrobot operating system (ROS)ultra-wideband (UWB)

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Area of Science:

  • Robotics and Automation
  • Signal Processing
  • Localization Systems

Background:

  • Ultra-wideband (UWB) systems offer high-precision localization capabilities.
  • Existing filtering methods like Moving Average (MVG), Kalman Filter (KF), and Extended Kalman Filter (EKF) have limitations in noise reduction and accuracy.
  • Enhancing UWB localization is crucial for applications in robotics, autonomous navigation, and indoor positioning.

Purpose of the Study:

  • To advance UWB localization systems by integrating filtering techniques.
  • To investigate novel approaches for improving the precision and reducing noise in UWB filtering.
  • To evaluate the performance of combined filtering methods, specifically Extended Kalman Filter with Low-Pass Filter (EKF + LPF), against traditional filters.

Main Methods:

  • Implementation of Moving Average (MVG), Kalman Filter (KF), Extended Kalman Filter (EKF), and a combined Extended Kalman Filter with Low-Pass Filter (EKF + LPF).
  • Testing the UWB system on a TurtleBot robotic platform across diverse trajectory paths (square, circular, free).
  • Quantitative analysis of localization accuracy using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).

Main Results:

  • The EKF + LPF demonstrated superior accuracy, achieving the lowest RMSE values (40.22 mm on X-axis, 78.71 mm on Y-axis) in square path trajectories.
  • The integrated LPF method consistently outperformed MVG, KF, and EKF.
  • Significant reduction in Mean Absolute Error (MAE) was observed: 3.39% for square, 4.21% for circular, and 6.16% for free paths.

Conclusions:

  • The combination of Extended Kalman Filter and Low-Pass Filter (EKF + LPF) is highly effective for enhancing UWB localization accuracy.
  • Integrated filtering approaches offer a robust solution for reducing noise and improving precision in UWB systems.
  • This research provides a validated method for achieving reliable indoor localization using UWB technology.