Passive indoor leveled RFID localization algorithms
Item
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Title
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Passive indoor leveled RFID localization algorithms
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Identifier
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d_2009_2013:c0fa8b2c6b20:11772
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identifier
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12448
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Creator
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Chan, Matthew,
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Contributor
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Xiaowen Zhang
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Date
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2013
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Language
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English
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Publisher
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City University of New York.
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Subject
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Computer science
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Abstract
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One of the most sought-after innovations in RFID technology is the ability to accurately locate stationary objects and track moving entities in real time. The author proposes three multi-leveled detectable count RFID localization algorithms (nearest-neighbor, multilateration, Bayesian inference) to accomplish these tasks using UHF passive RFID tags---chosen due to low cost and efficient implementation---by affixing them onto the floor as known reference nodes. Simulations are conducted to examine the accuracy and performance of the algorithms to locate stationary and mobile objects. Furthermore, experiments are carried out to test the localization of stationary objects in a real world setting such as a laboratory environment. The outcomes from the simulations and experiments are analyzed. The results are remarkable and most importantly, when the proper parametric values are considered, such as reference tag density and detection range, the accuracy performance of the algorithms achieved are impressive which confirms that the proposed methods are highly preferable when accurate, efficient and cost-effective passive RFID localization systems are to be implemented. Future directions of the study include exploration of different ratios for the three power levels of the RFID reader, use of other reference tag spacing pattern besides square such as hexagon, examination of other multi-level approach beside tri-level such as quad-, penta- or dual-level, experimentation with different kinds of RFID reference tags besides the passive Alien type G, as well as field tests of methods for mobile entities in a realistic real-world settings such as a laboratory.
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Type
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dissertation
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Source
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2009_2013.csv
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degree
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Ph.D.
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Program
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Computer Science
