Reference tag supported RFID tracking using robust support vector regression and Kalman filter

Jian Chai, Changzhi Wu, Chuanxin Zhao, Hung-lin Chi, Xiangyu Wang, Bingo Wing Kuen Ling, Kok Lay Teo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

51 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalAdvanced Engineering Informatics
Publication statusPublished - 1 Apr 2017
Externally publishedYes


  • Kalman fitler
  • RFID tracking
  • Support vector regression

ASJC Scopus subject areas

  • Information Systems
  • Artificial Intelligence


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