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Using Kalman filters to reduce noise from RFID location system.

Authors
  • Abreu, Pedro Henriques1
  • Xavier, José2
  • Silva, Daniel Castro2
  • Reis, Luís Paulo3
  • Petry, Marcelo2
  • 1 Department of Informatics Engineering, University of Coimbra/Centre for Informatics and Systems, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal. , (Portugal)
  • 2 Department of Informatics Engineering, Faculty of Engineering, University of Porto/LIACC-Artificial Intelligence and Computer Science Laboratory, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal. , (Portugal)
  • 3 Department of Information Systems, School of Engineering, University of Minho/LIACC-Artificial Intelligence and Computer, Science Laboratory, Campus de Azurm, 4800-058 Guimares, Portugal. , (Portugal)
Type
Published Article
Journal
The Scientific World JOURNAL
Publisher
Hindawi (The Scientific World)
Publication Date
Jan 01, 2014
Volume
2014
Pages
796279–796279
Identifiers
DOI: 10.1155/2014/796279
PMID: 24592186
Source
Medline
License
Unknown

Abstract

Nowadays, there are many technologies that support location systems involving intrusive and nonintrusive equipment and also varying in terms of precision, range, and cost. However, the developers some time neglect the noise introduced by these systems, which prevents these systems from reaching their full potential. Focused on this problem, in this research work a comparison study between three different filters was performed in order to reduce the noise introduced by a location system based on RFID UWB technology with an associated error of approximately 18 cm. To achieve this goal, a set of experiments was devised and executed using a miniature train moving at constant velocity in a scenario with two distinct shapes-linear and oval. Also, this train was equipped with a varying number of active tags. The obtained results proved that the Kalman Filter achieved better results when compared to the other two filters. Also, this filter increases the performance of the location system by 15% and 12% for the linear and oval paths respectively, when using one tag. For a multiple tags and oval shape similar results were obtained (11-13% of improvement).

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