Non-Line-of-Sight Localization of Passive UHF RFID Tags in Smart Storage Systems
Non-Line-of-Sight Localization of Passive UHF RFID Tags in Smart Storage Systems
复制标题
DOI：
10.1109/tmc.2021.3058952
复制
发表时间：
2022-10
影响因子：
7.9
通讯作者：
Linqing Gui;Shu-wen Xu;Fu Xiao;F. Shu;Shui Yu
中科院分区：
文献类型：
--
作者：
Linqing Gui;Shu-wen Xu;Fu Xiao;F. Shu;Shui Yu
在智能仓储系统中，超高频射频识别(RFID)在标签对象定位方面得到了越来越多的关注。由于非视距(NLOS)条件，标签在封闭空间内的精确定位是一项具有挑战性的工作。本文提出了一种在封闭空间中仅使用接收信号强度(RSS)信息进行标记对象定位的精确且经济有效的解决方案。我们为每个标签建立了RSS轮廓，并发现了RSS轮廓的一些重要特征，包括唯一性、时变性、列相关性和波形相似性。基于这些特点，我们提出了一种无引用RSS-Profile(RFRP)定位方案。该方案的优点是克服了缺乏预先部署的参考标签、NLOS传播、多路径传播和耦合效应等挑战，能够在封闭空间中精确定位多个标签。RFRP方案首先基于峰值不对称因子粗略估计标签的坐标，然后通过RSS序列的相似性来获得参考标签替代品。随后，我们的方案通过这些替换来精化所有标签的相对位置。最后，通过RSS测距模型估计出所有标签的绝对位置。大量实验结果表明，该方法对封闭空间内的标签具有较高的排序精度和定位精度。
The UHF radio-frequency identification (RFID) has gained growing attention for tagged object localization in smart storage systems. Due to Non-Line-Of-Sight (NLOS) condition, it is challenging to accurately locate the position of tags inside closed spaces. In this paper, we propose a precise and cost-effective solution for tagged object localization in closed spaces, using only received signal strength (RSS) information. We establish a RSS profile for each tag and discover some important features of RSS profiles including uniqueness, time-variation, column-dependence and waveform-similarity. Based on these features, we propose a reference-free RSS-profile (RFRP) localization scheme. The advantage of our propose scheme is to accurately localize multiple tags in closed spaces by overcoming the challenges including the lack of pre-deployed reference tags, NLOS propagation, multi-path propagation and coupling effect. The RFRP scheme first roughly estimates tags’ coordinates based on Peak Asymmetry Factor, then acquires reference-tag substitutes through the similarity of RSS sequences. Subsequently, our scheme refines the relative positions of all tags by these substitutes. Finally all tags’ absolute positions are estimated through a RSS-ranging model. Extensive experiment results demonstrate that our approach can achieve high ordering accuracy and localization accuracy for the tags inside closed spaces.