UHF RFID Localization Based on Evaluation of Backscattered Tag Signals

Scherhäufl, Martin; Pichler, Markus; Stelzer, Andreas

doi:10.1109/tim.2015.2440554

Cited by 58 publications

(26 citation statements)

References 25 publications

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“…Several indoor localization techniques were developed where an RSSI model (or path-loss model) proportional to 4 1 /d was assumed [32]- [34]. In more sophisticated models also accounting for the effect of multipath phenomena, a more general 1 n /d model has been assumed where the path-loss exponent n is heuristically estimated on the basis of extended measurement campaigns or numerical simulations.…”

Section: Rssi Modelmentioning

confidence: 99%

RSSI Measurements for RFID Tag Classification in Smart Storage Systems

Buffi

Michel

Nepa

et al. 2018

IEEE Trans. Instrum. Meas.

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This paper presents a measurement method for tagged item classification in radio frequency identification (RFID)-based smart drawers. Specifically, the amplitude of the signal backscattered by the UHF-RFID passive tags attached to the items inside the drawer is used to identify at which of an assigned set of drawer subregions each detected tag belongs. The received signal strength indicator (RSSI) may be acquired by almost any UHF-RFID commercial reader chipset compatible with the Electronic Product Code (EPC) Class1 Gen2 protocol. The RSSI model in terms of the position of the tag inside the drawer is introduced, and its selective properties are discussed through both numerical data and measurements. The cases of a single reader antenna measuring during natural opening/closing operations as well as of multiple reader antennas measuring when the drawer is static are investigated. The measurement method performance is shown through an experimental analysis in a realistic scenario, where the RSSI values are collected by using commercial UHF RFID hardware components, and then used as the feature vector of the classification algorithm. As an example, two different classifiers are used to map each detected tag to one of the regions the drawer is subdivided into. The classification performance of the method based on a single antenna exploiting the drawer natural sliding movements is comparable to that achievable with multiple reader antennas, while representing a cost-effective and easy-to-implement practical solution. Index Terms-Antenna near-field region, classification techniques, radio frequency identification (RFID) tag classification, received signal strength indicator (RSSI) measurements, RFID tag indoor localization, RFID-based smart drawers, RFID-based smart storage spaces, UHF-RFID measurements.Andrea Michel (S'08-M'15) received the B.E., M.E. (summa cum laude) and PhD degrees in telecommunications engineering from the University of Pisa, Pisa, Italy, in 2009, 2011 Since 2015 he has been a Post-Doctoral Researcher in applied electromagnetism at the Microwave and Radiation Laboratory, Department of Information Engineering, University of Pisa, focusing on the design of integrated antenna for communication systems and smart antennas for near-field UHF-radio frequency identification (RFID) readers. In 2014, he joined the ElectroScience Laboratory, The Ohio State University, Columbus, OH, USA, as a Visiting Scholar, where he has been involved in research on a theoretical analysis on the accuracy of a novel technique for deep tissue imaging. His current research interests include the design of antennas for automotive applications, multiple-in multiple-out systems, and wearable communication systems, in collaboration with other research institutes and companies.Dr. Michel received the Young Scientist Paolo Nepa (M'07) received the Laurea (Doctor) degree (summa cum laude) in electronics engineering from the University of Pisa, Italy, in 1990. Since 1990, he has been with the Department of Information Engineering, Uni...

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Section: Rssi Modelmentioning

confidence: 99%

RSSI Measurements for RFID Tag Classification in Smart Storage Systems

Buffi

Michel

Nepa

et al. 2018

IEEE Trans. Instrum. Meas.

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“…There have been various studies investigating the ability to implement the algorithms and methods for tag localization using different RFID techniques in practice [17,18]. Regarding the requirement for high accurate indoor localization, the traditional methods are not suitable to be adaptive for indoorlocalization using passive RFID technology.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the requirement for high accurate indoor localization, the traditional methods are not suitable to be adaptive for indoorlocalization using passive RFID technology. The reason is that these traditional methods may either use a simple algorithm [8] or need more hardware such as reference tags or additional reader antennas to maintain the environment changes, or use complex techniques [17] with additional hardware [18]. Most studies [10,15,19] using K-NN for UHF RFID location sensing rely on the measured RSSI value at the frequency of 915 MHz, and power strength at this frequency is used as a classification feature.…”

Section: Introductionmentioning

confidence: 99%

Indoor Localization Systems for Passive Uhf Rfid Tag Based on Rssi Radio Map Database

Omer¹,

Ran²,

Tian³

2019

PIER M

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A classification algorithm is applied to UHF radio frequency identification (RFID) system to estimate the indoor position of a passive tag utilizing a received signal strength indicator (RSSI). Passive tag signals are collected by conventional UHF RFID reader antenna located at two different positions. K-NN and curve fitting are used for distance estimation and examined at different frequencies within the range 902-928 MHz to find suitable frequencies which can minimize the average error. In the proposed method, the measured RSSI values are compared with the fingerprint database in different frequencies to find the nearest neighbor. The best accuracy achieved at frequency range 926-928 MHz is 18.3 cm. The monitoring system is composed of a reader and tags under test, which makes the proposed system robust, easy to set up, and with low cost. The limitation of the proposed method is also discussed.

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“…A large number of nodes should be arranged for indoor positioning, and each node also needs to supply power alone, and the network is complex, it is not suitable for positioning the educational robot in the classroom scenario. Many modern applications use RFID as a wireless non-contact method of data transfer to identify and track the objects to which the transponders are attached [8]. Passive tags can be used, which obtain the energy from the interrogating electromagnetic field created by RFID readers, and the transponders require no local power sources, such as batteries, which results in the reduced need for maintenance while achieving high flexibility, long lifetime, and low cost.…”

Section: Introductionmentioning

confidence: 99%

Positioning and guiding educational robots by using fingerprints of WiFi and RFID array

Luo

Zhang

et al. 2018

J Wireless Com Network

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Positioning educational robots in the indoor ambiance is an important and basic function for robots to provide intelligent educational services for users, and it is still an open challenge problem. This paper proposes to position indoor robots by using fingerprints of wireless fidelity (WiFi) and radio-frequency identification (RFID) array in the complementary way. The fingerprint of WiFi is first used to position educational robots (Erob) in a large area and to guide robots to a place close to the target location. Then, the fingerprint of RFID array is used to guide Erob to the target location with a small discrepancy. It proved that the designed layouts of WiFi devices and RFID array can have fingerprint matching to estimate the position of Erob. The proposed positioning method can guide Erob to the target location fast and accurately so that robots can provide multiple services based on it.

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UHF RFID Localization Based on Evaluation of Backscattered Tag Signals

Cited by 58 publications

References 25 publications

RSSI Measurements for RFID Tag Classification in Smart Storage Systems

RSSI Measurements for RFID Tag Classification in Smart Storage Systems

Indoor Localization Systems for Passive Uhf Rfid Tag Based on Rssi Radio Map Database

Positioning and guiding educational robots by using fingerprints of WiFi and RFID array

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