Paper Info
Title
Reference tag supported RFID tracking using robust support vector regression and Kalman filter.
Abstract
Site 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.
Year
DOI
Venue
2017
10.1016/j.aei.2016.11.002
Advanced Engineering Informatics
Keywords
Field
DocType
RFID tracking,Kalman fitler,Support vector regression
Data mining,Support vector machine,Outlier,Kalman filter,Interference (wave propagation),Liquefied natural gas,Engineering,Radio-frequency identification
Journal
Volume
Issue
ISSN
32
C
1474-0346
Citations 
PageRank 
References 
17
0.85
22
Authors
7
Name
Order
Citations
PageRank
Jian Chai1466.48
Changzhi Wu219519.07
Chuanxin Zhao3485.24
Hung-Lin Chi4244.11
Xiangyu Wang5778.35
Bingo Wing-Kuen Ling623350.78
K. L. Teo71643211.47