UHF RFID tag localization using pattern reconfigurable reader antenna.

Md Shakir Hossain, Md Abu Saleh Tajin, Kapil R Dandekar
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引用次数: 3

Abstract

Passive ultra high frequency (UHF) radio frequency identification (RFID) tags have the potential to find ubiquitous use in indoor object tracking, localization, and contact tracing. We propose a machine learning-based method for RFID indoor localization using a pattern reconfigurable UHF RFID reader antenna array. The received signal strength indicator (RSSI) values (from 10,000 tags) recorded at the reader antenna units are used as features to evaluate the machine learning models with a train-test split of 75%-25%. The training and testing data is generated by a wireless ray tracing simulator. Five machine learning models: random forest regressor, decision tree regressor, Nu support vector regressor, k nearest regressor, and kernel ridge regressor are compared. Random forest regressor has the lowest localization error both in terms of average Euclidean distance (AED) and root-mean-square error (RMSE). For random forest regressor, localization error results show that 90% of the tags are within 1 meter of their true position, and 67% are within 50 cm of their true position based on Euclidean distance.

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使用模式可重构阅读器天线的超高频RFID标签定位。
无源超高频(UHF)射频识别(RFID)标签有可能在室内物体跟踪,定位和接触追踪中无处不在。我们提出了一种基于机器学习的RFID室内定位方法,使用模式可重构的UHF RFID读取器天线阵列。在读取器天线单元记录的接收信号强度指标(RSSI)值(来自10,000个标签)被用作特征,以75%-25%的训练测试分割来评估机器学习模型。训练和测试数据由无线射线追踪模拟器生成。比较了随机森林回归器、决策树回归器、Nu支持向量回归器、k最近回归器和核脊回归器五种机器学习模型。随机森林回归量在平均欧氏距离(AED)和均方根误差(RMSE)方面具有最低的定位误差。对于随机森林回归器,定位误差结果显示,基于欧氏距离,90%的标签距离真实位置在1米以内,67%的标签距离真实位置在50厘米以内。
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