Reference tag supported RFID tracking using robust support vector regression and Kalman filter
© 2016 Elsevier LtdSite operations usually contain potential safety issues and an effective monitoring strategy for operations is essential to predict and prevent risk. Regarding the status monitoring among material, equipment and personnel during site operations, much work is conducted on localizat...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Published: |
Pergamon Press
2017
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| Online Access: | http://purl.org/au-research/grants/arc/LP130100451 http://hdl.handle.net/20.500.11937/50833 |
| _version_ | 1848758546882101248 |
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| author | Chai, J. Wu, Changzhi Zhao, C. Chi, H. Wang, X. Ling, B. Teo, K. |
| author_facet | Chai, J. Wu, Changzhi Zhao, C. Chi, H. Wang, X. Ling, B. Teo, K. |
| author_sort | Chai, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2016 Elsevier LtdSite operations usually contain potential safety issues and an effective monitoring strategy for operations is essential to predict and prevent risk. Regarding the status monitoring among material, equipment and personnel during site operations, much work is conducted on localization and tracking using Radio Frequency Identification (RFID) technology. However, existing RFID tracking methods suffer from low accuracy and instability, due to severe interference in industrial sites with many metal structures. To improve RFID tracking performance in industrial sites, a RFID tracking method that integrates Multidimensional Support Vector Regression (MSVR) and Kalman filter is developed in this paper. Extensive experiments have been conducted on a Liquefied Natural Gas (LNG) facility site with long range active RFID system to evaluate the performance of this approach. The results demonstrate the effectiveness and stability of the proposed approach with severe noise and outliers. It is feasible to adopt the proposed approach which satisfies intrinsically-safe regulations for monitoring operation status in current practice. |
| first_indexed | 2025-11-14T09:45:43Z |
| format | Journal Article |
| id | curtin-20.500.11937-50833 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:45:43Z |
| publishDate | 2017 |
| publisher | Pergamon Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-508332023-02-02T03:24:11Z Reference tag supported RFID tracking using robust support vector regression and Kalman filter Chai, J. Wu, Changzhi Zhao, C. Chi, H. Wang, X. Ling, B. Teo, K. © 2016 Elsevier LtdSite operations usually contain potential safety issues and an effective monitoring strategy for operations is essential to predict and prevent risk. Regarding the status monitoring among material, equipment and personnel during site operations, much work is conducted on localization and tracking using Radio Frequency Identification (RFID) technology. However, existing RFID tracking methods suffer from low accuracy and instability, due to severe interference in industrial sites with many metal structures. To improve RFID tracking performance in industrial sites, a RFID tracking method that integrates Multidimensional Support Vector Regression (MSVR) and Kalman filter is developed in this paper. Extensive experiments have been conducted on a Liquefied Natural Gas (LNG) facility site with long range active RFID system to evaluate the performance of this approach. The results demonstrate the effectiveness and stability of the proposed approach with severe noise and outliers. It is feasible to adopt the proposed approach which satisfies intrinsically-safe regulations for monitoring operation status in current practice. 2017 Journal Article http://hdl.handle.net/20.500.11937/50833 10.1016/j.aei.2016.11.002 http://purl.org/au-research/grants/arc/LP130100451 Pergamon Press restricted |
| spellingShingle | Chai, J. Wu, Changzhi Zhao, C. Chi, H. Wang, X. Ling, B. Teo, K. Reference tag supported RFID tracking using robust support vector regression and Kalman filter |
| title | Reference tag supported RFID tracking using robust support vector regression and Kalman filter |
| title_full | Reference tag supported RFID tracking using robust support vector regression and Kalman filter |
| title_fullStr | Reference tag supported RFID tracking using robust support vector regression and Kalman filter |
| title_full_unstemmed | Reference tag supported RFID tracking using robust support vector regression and Kalman filter |
| title_short | Reference tag supported RFID tracking using robust support vector regression and Kalman filter |
| title_sort | reference tag supported rfid tracking using robust support vector regression and kalman filter |
| url | http://purl.org/au-research/grants/arc/LP130100451 http://hdl.handle.net/20.500.11937/50833 |