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The Scientific World Journal
Volume 2014, Article ID 796279, 9 pages
http://dx.doi.org/10.1155/2014/796279
Research Article

Using Kalman Filters to Reduce Noise from RFID Location System

1Department of Informatics Engineering, University of Coimbra/Centre for Informatics and Systems, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal
2Department of Informatics Engineering, Faculty of Engineering, University of Porto/LIACC-Artificial Intelligence and Computer Science Laboratory, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
3Department of Information Systems, School of Engineering, University of Minho/LIACC-Artificial Intelligence and Computer, Science Laboratory, Campus de Azurm, 4800-058 Guimares, Portugal

Received 29 August 2013; Accepted 27 November 2013; Published 27 January 2014

Academic Editors: G. R. Amin, H. Chen, and F. Di Martino

Copyright © 2014 Pedro Henriques Abreu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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).