Passive UHF-RFID Localization Based on the Similarity Measurement of Virtual Reference Tags

2019 ◽  
Vol 68 (8) ◽  
pp. 2926-2933 ◽  
Author(s):  
Lingfei Mo ◽  
Chenyang Li
Keyword(s):  
Similarity Measurement ◽  
Uhf Rfid ◽  
Virtual Reference ◽  
Passive Uhf Rfid ◽  
Reference Tags

Indoor Localization Using the Reference Tags and Phase of Passive UHF-RFID Tags

2017 ◽  
Vol 13 (2) ◽  
pp. 69-82 ◽  
Author(s):  
He Xu ◽  
Ye Ding ◽  
Peng Li ◽  
Ruchuan Wang
Keyword(s):  
Production Management ◽  
Indoor Localization ◽  
Rfid Tags ◽  
Uhf Rfid ◽  
Passive Uhf Rfid ◽  
Hardware System ◽  
Reference Tags ◽  
Software And Hardware ◽  
Collaborative Information System ◽  
Time And Energy

In recent years, indoor position has been an important role in many applications, such as production management, store management and shelves in supermarket or library. Much time and energy are exhausted because one object cannot be quickly and accurately located. Traditional indoor position systems have some problems, such as complicated software and hardware system, inaccurate position and high time complexity. In this paper, the authors propose an RFID-based collaborative information system, Tagrom, for indoor localization using COTS RFID readers and tags. Unlike former methods, Tagrom works with reference tags and phase of Passive UHF-RFID tags, which improves traditional distribution of reference tags and utilize RF phase replace of traditional RSSI or multipath profile to determine the position of target RFID tags.

scholarly journals Localization of passive UHF RFID Labels with Kalman Filter

2012 ◽  
Vol 10 ◽  
pp. 119-125 ◽  
Author(s):  
T. Nick ◽  
J. Götze
Keyword(s):  
Kalman Filter ◽  
Radio Frequency Identification ◽  
Unscented Kalman Filter ◽  
Uhf Rfid ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Passive Uhf Rfid ◽  
Reader Antenna ◽  
Reference Tags ◽  
Frequency Identification

Abstract. Localization via Radio Frequency Identification (RFID) is frequently used in different applications nowadays. It has the advantage that next to its ostensible purpose of identifying objects via their unique IDs it can simultaneously be used for the localization of these objects. In this work it is shown how Received Signal Strength Indicator (RSSI) measurements at different antennae of a passive UHF RFID label can be combined for localization. The localization is only done based on the RSSI measurements and a Kalman Filter (KF). Because of non-linearities in the measurement function it is necessary to incorporate an Extended Kalman Filter (EKF) or an Unscented Kalman Filter (UKF) where simulations have shown that the UKF performs better than the EKF. Additionally to the selection of the filter there are different possibilities to increase the localization accuracy of the UKF: The advantages of using Reference Tags (RT) or more than one tag per trolley (relative positioning) in combination with an Unscented Kalman Filter are discussed and simulations results show that the localization error can be decreased significantly via these methods. Another possibility to increase the localization accuracy and in addition to achieve a more realistic simulation is the consideration of the angle between reader antenna and tag. Simulation results with the incorporation of different numbers of fixed antennae lead to the conclusion that this is a useful surplus in the localization.

Modified RSSI Technique for the Localization of Passive UHF RFID Tags in LOS Channels

2013 ◽  
Vol 5 (5) ◽  
pp. 645-651 ◽  
Author(s):  
Y. Duroc ◽  
G. Andia Vera ◽  
J. P. Garcia Martin
Keyword(s):  
Radio Frequency Identification ◽  
Simple Algorithm ◽  
Rfid Tags ◽  
Uhf Rfid ◽  
Unknown Parameters ◽  
Received Signal Strength Indicator ◽  
Passive Uhf Rfid ◽  
Reference Tags ◽  
Frequency Identification ◽  
Complex Propagation

This paper presents a new approach for improving the localization of passive ultra high frequency radio frequency identification (RFID) tags in line-of-sight channels using a received signal strength indicator (RSSI) technique. In practice, the complex propagation in the indoor channels and also the variability of some parameters of the RFID equipment itself introduces significant amount of errors when the operation of localization carries out the RSSI technique. Indeed, as the calculation is based on a trilateration, the incomplete knowledge of the propagation and some parameters of RFID tags leads to estimate distances which are wrong, and therefore the localization cannot be correct. In order to overcome this drawback, the proposed method takes into account the presence of unknown parameters relying on a dichotomous algorithm which includes probabilistic parameters. The presented simulation results are in good agreement with the expected theoretical results. Experimental results show that the proposed method strongly increases the accuracy of the estimated position of tags. Compared to other approaches based on the improvement of the RSSI technique, this method does not require too much complexity in terms of materials (no need for specific architecture or reference tags) and processing (fast and simple algorithm).

Design and implementation of a passive UHF RFID tag with a temperature and process compensation oscillator

2015 ◽  
Vol 6 (4) ◽  
pp. 171-184
Author(s):  
Liangbo Xie ◽  
Jiaxin Liu ◽  
Yao Wang ◽  
Chuan Yin ◽  
Guangjun Wen
Keyword(s):  
Uhf Rfid ◽  
Rfid Tag ◽  
Passive Uhf Rfid ◽  
Design And Implementation

Design of temperature sensor embedded in Passive UHF RFID Tag

2011 ◽  
Vol 25 (5) ◽  
pp. 468-473
Author(s):  
Weifeng Liu ◽  
Yiqi Zhuang ◽  
Zengwei Qi ◽  
Longfei Tang
Keyword(s):  
Temperature Sensor ◽  
Uhf Rfid ◽  
Rfid Tag ◽  
Passive Uhf Rfid

scholarly journals Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach †

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 717
Author(s):  
Farhad Shamsfakhr ◽  
Andrea Motroni ◽  
Luigi Palopoli ◽  
Alice Buffi ◽  
Paolo Nepa ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Uhf Rfid ◽  
Missing Measurements ◽  
Kalman Smoother ◽  
Vehicle Trajectories ◽  
Angular Velocities ◽  
Reference Tags ◽  
Entire Trajectory ◽  
Method Accuracy ◽  
Mean Square Errors

Autonomous vehicles enable the development of smart warehouses and smart factories with an increased visibility, flexibility and efficiency. Thus, effective and affordable localisation methods for indoor vehicles are attracting interest to implement real-time applications. This paper presents an Extended Kalman Smoother design to both localise a mobile agent and reconstruct its entire trajectory through a sensor-fusion employing the UHF-RFID passive technology. Extensive simulations are carried out by considering the smoother optimal-window length and the effect of missing measurements from reference tags. Monte Carlo simulations are conducted for different vehicle trajectories and for different linear and angular velocities to evaluate the method accuracy. Then, an experimental analysis with a unicycle wheeled robot is performed in real indoor scenario, showing a position and orientation root mean square errors of 15 cm, and 0.2 rad, respectively.

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