Back to Search Start Over

Investigation of RFID Readability for License Plates in Static and Motion Testing

Authors :
Venkatesan, Srinivasan
Publication Year :
2011

Abstract

The most important function of RFID in vehicle tracking is to store information concerning tagged elements in order to improve the overall performance of movable asset management. There is a need to discover an RFID system for the tracking of vehicles, as existing vehicle tracking systems are undependable. Experiments were performed with SIRIT, SAVI, and RF code RFID systems under differing conditions, attaching the tag to the license plate instead of the windshield. Different spacers were also tested to reduce the effect of metal surfaces on RFID signals. Preliminary experiments were performed before stationary and motion testing in order to better understand the RFID systems. Testing was also conducted to identify the angle at which the reader should be fixed and the ideal placement of the tag on the license plate. Stationary and motion testing were then performed on the three RFID systems, using different spacers and speeds, and the effect of spacers and speed on signal strength was found to be significant. In addition, environmental testing was performed on RF code systems in low temperature conditions. Upon completion of these experiments, the resulting data was analyzed to identify not only the best material to embed between tag and license plate in practical situations, but also the most effective thickness of that material and the optimum height of the reader. Finally, a benefit cost analysis was performed comparing both the RF code RFID and mobile plate hunter (MPH)-900 camera system. Results were compared for varying amounts of cars, and the analysis clearly showed that the RFID RF code system is better, compared to camera systems, for use in the tracking of vehicle license plate. Adviser: Robert E. Williams

Details

Language :
English
Database :
OpenDissertations
Publication Type :
Dissertation/ Thesis
Accession number :
ddu.oai.digitalcommons.unl.edu.imsediss.1022