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

Barrett Durtschi, Mahesh Mahat, M. Mashal, Andrew M. Chrysler
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引用次数: 3

Abstract

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.
基于多重标签和加权欧氏距离k-NN算法的移动预制混凝土单元RFID定位系统初步分析
本文提出了两种基于k-NN算法的RFID定位方法,用于定位混凝土砌块(煤渣砖)上的多个移动跟踪标签。这项工作使用无源RFID标签进行定位,并寻求为未来的智能基础设施项目提供快速无线分析,其中预制混凝土模块化结构在运输和组装过程中移动。RFID定位系统在真实的室内装配环境中使用四个读取器天线,四个跟踪标签和28个参考标签。结果表明,移动方向的平均误差低至10.5 cm。在k-NN算法中,增加最近邻的数量可以减少所有坐标方向上的误差。将k从4增加到6可以减少4厘米或10%的误差。分析了定位环境,移动煤渣砖周围的参考标签22、9、5和8通常被视为最近的邻居。提出了一种改进的k -NN算法,本文将其描述为加权欧几里得距离k -NN算法,该算法将总误差从41.1 cm减少到32.5 cm。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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