Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor Networks
The Extended Kalman Filter (EKF) has received abundant attention with the growing demands for robotic localization. The EKF algorithm is more realistic in non-linear systems, which has an autonomous white noise in both the system and the estimation model. Also, in the field of engineering, most syst...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2021-05-01
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| Series: | Digital Communications and Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864820302601 |
| _version_ | 1818435988568932352 |
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| author | Inam Ullah Siyu Qian Zhixiang Deng Jong-Hyouk Lee |
| author_facet | Inam Ullah Siyu Qian Zhixiang Deng Jong-Hyouk Lee |
| author_sort | Inam Ullah |
| collection | DOAJ |
| description | The Extended Kalman Filter (EKF) has received abundant attention with the growing demands for robotic localization. The EKF algorithm is more realistic in non-linear systems, which has an autonomous white noise in both the system and the estimation model. Also, in the field of engineering, most systems are non-linear. Therefore, the EKF attracts more attention than the Kalman Filter (KF). In this paper, we propose an EKF-based localization algorithm by edge computing, and a mobile robot is used to update its location concerning the landmark. This localization algorithm aims to achieve a high level of accuracy and wider coverage. The proposed algorithm is helpful for the research related to the use of EKF localization algorithms. Simulation results demonstrate that, under the situations presented in the paper, the proposed localization algorithm is more accurate compared with the current state-of-the-art localization algorithms. |
| first_indexed | 2024-12-14T17:01:38Z |
| format | Article |
| id | doaj.art-492071069a094b2fb7f3158db6eac8fb |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-12-14T17:01:38Z |
| publishDate | 2021-05-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-492071069a094b2fb7f3158db6eac8fb2022-12-21T22:53:50ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482021-05-0172187195Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor NetworksInam Ullah0Siyu Qian1Zhixiang Deng2Jong-Hyouk Lee3College of Internet of Things (IoT) Engineering, Hohai University, Changzhou Campus, 213022, ChinaCollege of Internet of Things (IoT) Engineering, Hohai University, Changzhou Campus, 213022, ChinaCollege of Internet of Things (IoT) Engineering, Hohai University, Changzhou Campus, 213022, ChinaDepartment of Computer and Information Security, Sejong University, Seoul, 05006, Republic of Korea; Corresponding author.The Extended Kalman Filter (EKF) has received abundant attention with the growing demands for robotic localization. The EKF algorithm is more realistic in non-linear systems, which has an autonomous white noise in both the system and the estimation model. Also, in the field of engineering, most systems are non-linear. Therefore, the EKF attracts more attention than the Kalman Filter (KF). In this paper, we propose an EKF-based localization algorithm by edge computing, and a mobile robot is used to update its location concerning the landmark. This localization algorithm aims to achieve a high level of accuracy and wider coverage. The proposed algorithm is helpful for the research related to the use of EKF localization algorithms. Simulation results demonstrate that, under the situations presented in the paper, the proposed localization algorithm is more accurate compared with the current state-of-the-art localization algorithms.http://www.sciencedirect.com/science/article/pii/S2352864820302601Extended Kalman filterEdge computingKalman filterLocalizationRobotsState estimation |
| spellingShingle | Inam Ullah Siyu Qian Zhixiang Deng Jong-Hyouk Lee Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor Networks Digital Communications and Networks Extended Kalman filter Edge computing Kalman filter Localization Robots State estimation |
| title | Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor Networks |
| title_full | Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor Networks |
| title_fullStr | Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor Networks |
| title_full_unstemmed | Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor Networks |
| title_short | Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor Networks |
| title_sort | extended kalman filter based localization algorithm by edge computing in wireless sensor networks |
| topic | Extended Kalman filter Edge computing Kalman filter Localization Robots State estimation |
| url | http://www.sciencedirect.com/science/article/pii/S2352864820302601 |
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