用于无源超高频RFID标签测距的人工神经网络

2013 11th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (TELSIKS) Pub Date : 2013-10-01 DOI:10.1109/TELSKS.2013.6704428

M. Agatonovic, E. Di Giampaolo, P. Tognolatti, B. Milovanovic

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引用次数: 5

摘要

无源超高频(UHF)射频识别(RFID)标签在室内环境中的测距是当今的热点问题。由于这种环境的复杂性，目前还没有有效的解决方案。本文研究了人工神经网络在无源超高频RFID标签室内定位中的应用。也就是说，我们估计阅读器天线和附着在物品上的几个标签之间的距离，使用ann在接收信号强度指示器(RSSI)的测量值之间执行的非线性映射，一方面打开电源和相位，另一方面打开距离。该模型计算距离的平均误差为7.31 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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Artificial Neural Networks for ranging of passive UHF RFID tags

Ranging of passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) tags in indoor environments is a topical issue nowadays. Due to complexity of such an environment, there is no effective solution to this problem. In this paper we investigate application of Artificial Neural Networks (ANNs) in indoor localization of passive UHF RFID tags. Namely, we estimate distance between a reader antenna and a couple of tags attached to an item, using nonlinear mapping that ANNs perform between measured values of the Received Signal Strength Indicator (RSSI), turn on power and phase on the one hand, and the distance on the other. The proposed ANN model calculates distance with an average error of 7.31 cm.

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2013 11th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (TELSIKS)

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