Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment
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...
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MDPI AG
2024-02-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1052 |
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| author | Rahul Ranjan Donggyu Shin Yoonsik Jung Sanghyun Kim Jong-Hwan Yun Chang-Hyun Kim Seungjae Lee Joongeup Kye |
| author_facet | Rahul Ranjan Donggyu Shin Yoonsik Jung Sanghyun Kim Jong-Hwan Yun Chang-Hyun Kim Seungjae Lee Joongeup Kye |
| author_sort | Rahul Ranjan |
| collection | DOAJ |
| description | 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 <i>X</i> axis, and 78.71 mm on the <i>Y</i> 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. |
| first_indexed | 2024-03-07T22:15:28Z |
| format | Article |
| id | doaj.art-f331ba783d2c481fb4bc4d790bcb4731 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:15:28Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f331ba783d2c481fb4bc4d790bcb47312024-02-23T15:33:27ZengMDPI AGSensors1424-82202024-02-01244105210.3390/s24041052Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor EnvironmentRahul Ranjan0Donggyu Shin1Yoonsik Jung2Sanghyun Kim3Jong-Hwan Yun4Chang-Hyun Kim5Seungjae Lee6Joongeup Kye7Department of Computer and Electronic Convergence, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of KoreaDepartment of Computer Engineering, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of KoreaDepartment of Computer Engineering, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of KoreaDepartment of Mechanical Engineering, Kyung Hee University, Suwon 17104, Republic of KoreaMobility Materials-Parts-Equipment Centre, Kongju National University, Kongju 32588, Republic of KoreaDepartment of AI Machinery, Korea Institute of Machinery and Materials, Daejeon 34103, Republic of KoreaDepartment of Computer Engineering, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of KoreaDepartment of Mechanical Engineering, Sun Moon University, Asan 31460, Republic of KoreaThis 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 <i>X</i> axis, and 78.71 mm on the <i>Y</i> 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.https://www.mdpi.com/1424-8220/24/4/1052ultra-wideband (UWB)moving average filter (MVG)Kalman filter (KF)extended Kalman filter (EKF)robot operating system (ROS)LiDAR |
| spellingShingle | Rahul Ranjan Donggyu Shin Yoonsik Jung Sanghyun Kim Jong-Hwan Yun Chang-Hyun Kim Seungjae Lee Joongeup Kye Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment Sensors ultra-wideband (UWB) moving average filter (MVG) Kalman filter (KF) extended Kalman filter (EKF) robot operating system (ROS) LiDAR |
| title | Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment |
| title_full | Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment |
| title_fullStr | Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment |
| title_full_unstemmed | Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment |
| title_short | Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment |
| title_sort | comparative analysis of integrated filtering methods using uwb localization in indoor environment |
| topic | ultra-wideband (UWB) moving average filter (MVG) Kalman filter (KF) extended Kalman filter (EKF) robot operating system (ROS) LiDAR |
| url | https://www.mdpi.com/1424-8220/24/4/1052 |
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