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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

This research delves into advancing an ultra-wideband (UWB) localization system through the integration of filtering technologies (moving average (MVG), Kalman filter (KF), extended Kalman filter (EKF)) with a low-pass filter (LPF). We investigated new approaches to enhance the precision and reduce noise of the current filtering methods—MVG, KF, and EKF. Using a TurtleBot robotic platform with a camera, our research thoroughly examines the UWB system in various trajectory situations (square, circular, and free paths with 2 m, 2.2 m, and 5 m distances). Particularly in the square path trajectory with the lowest root mean square error (RMSE) values (40.22 mm on the X axis, and 78.71 mm on the Y axis), the extended Kalman filter with low-pass filter (EKF + LPF) shows notable accuracy. This filter stands out among the others. Furthermore, we find that integrated method using LPF outperforms MVG, KF, and EKF consistently, reducing the mean absolute error (MAE) to 3.39% for square paths, 4.21% for circular paths, and 6.16% for free paths. This study highlights the effectiveness of EKF + LPF for accurate indoor localization for UWB systems.

Details

Title
Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment
Author
Ranjan, Rahul 1 ; Shin, Donggyu 2 ; Jung, Yoonsik 2 ; Kim, Sanghyun 3   VIAFID ORCID Logo  ; Jong-Hwan, Yun 4   VIAFID ORCID Logo  ; Chang-Hyun, Kim 5   VIAFID ORCID Logo  ; Lee, Seungjae 2 ; Joongeup Kye 6 

 Department of Computer and Electronic Convergence, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of Korea; [email protected] 
 Department of Computer Engineering, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of Korea; [email protected] (D.S.); [email protected] (Y.J.) 
 Department of Mechanical Engineering, Kyung Hee University, Suwon 17104, Republic of Korea; [email protected] 
 Mobility Materials-Parts-Equipment Centre, Kongju National University, Kongju 32588, Republic of Korea; [email protected] 
 Department of AI Machinery, Korea Institute of Machinery and Materials, Daejeon 34103, Republic of Korea; [email protected] 
 Department of Mechanical Engineering, Sun Moon University, Asan 31460, Republic of Korea; [email protected] 
First page
1052
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2931104469
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.