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Preliminary Analysis of RFID Localization System for Moving Precast Concrete Units using Multiple-Tags and Weighted Euclid Distance k-NN algorithm

机译:使用多标签和加权欧几径k-nn算法移动预制混凝土单元的RFID定位系统的初步分析

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This paper presents two RFID localization methods based on a k-NN algorithm for multiple moving tracking tags attached to a concrete masonry unit (cinder block). This work uses passive RFID tags for localization and seeks to provide rapid wireless analysis for future smart infrastructure projects where precast concrete modular structures are moved during transport and assembly. The RFID localization system uses four reader antennas, four tracking tags, and 28 reference tags in a realistic indoor assembly environment. Results show average error in the direction of movement as low as 10.5 cm. Increasing the number of nearest neighbors in the k-NN algorithm is shown to reduce error in all coordinate directions. Increasing k from 4 to 6 is shown to reduce error by 4 cm or 10%. The localization environment is analyzed, and reference tags 22, 9, 5, and 8 around the moving cinder block are seen most commonly as nearest neighbors. A modified k -NN algorithm, described here as a weighted Euclidian distance k -NN algorithm is presented that reduces total error from 41.1 cm to 32.5 cm.
机译:本文介绍了基于K-NN算法的两种RFID定位方法,用于连接到混凝土砌体单元(煤渣块)的多个移动跟踪标签。这项工作使用被动RFID标签来定位,并寻求为未来的智能基础设施项目提供快速的无线分析,其中预制混凝土模块化结构在运输和组装过程中移动。 RFID定位系统使用四个读者天线,四个跟踪标签和28个参考标签在现实的室内装配环境中。结果显示电量方向的平均误差低至10.5厘米。增加了K-NN算法中最近邻居的数量,显示为在所有坐标方向上减小误差。增加k从4到6的k显示为减少4厘米或10%的误差。分析定位环境,以及在移动的煤渣块周围的附图标记22,9,5和8是最常见的作为最近的邻居。提出了一种修改的k-nn算法作为加权欧几里德距离k-nn算法,其将总误差从41.1cm到32.5cm降低。

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