捕获效应下的RFID标签识别最优效率研究

发布时间：2019-07-03 18:55

【摘要】：射频识别（RFID，，Radio Frequency Identification）技术是一种非接触式自动识别技术。射频识别技术作为物联网（IoT，Internet of Things）的关键技术之一，被广泛应用在数据采集和商品识别领域。今天，随着物流供应链中物品种类以及数量的急速增长，迫切需要阅读器在较短的时间内高效率、快速地识别阅读器可识别范围内的大量标签。这也导致了标签阅读冲突而造成系统识别效率低下问题。然而，系统中由于诸多原因会引起捕获效应，例如系统中标签距离阅读器的远近差异，而如何利用捕获效应提高系统识别效率成了RFID领域最炙手可热的研究课题。本文基于动态帧时隙ALOHA协议（DFSA， Dynamic Framed SlottedALOHA），针对具有捕获效应的被动式RFID系统防冲突标签估计算法进行了系统性研究。本文提出了：（1）一种考虑捕获效应的防冲突标签数估计算法：2-DBayes算法，该算法观测并统计空时隙、成功时隙以及冲突时隙数，通过二维最大化后验概率搜索估计系统待识别标签数以及当前系统捕获效应概率值；（2）一种考虑捕获效应下的低计算复杂度的标签数估计算法：CAE（Capture-awareEstimation）算法，该算法通过观测空时隙数来估计系统中的标签数，然后通过所得标签数估计值来估计系统的捕获效应概率值；（3）一种解决在空时隙数为零的情况下传统算法均失效的帧长重调算法：ECAE（Enhanced Capture-awareEstimation）算法，该算法通过bit-slot方法询问待识别标签，并通过设置最小概率事件阈值进行扩帧，快速有效地使空时隙数不为零，保证了CAE算法的有效性；（4）一种非等长时隙条件下的线性最优帧长设置方案，该方案兼容ISO18000-6c标准和EPC C1Gen2标准，即各个时隙非等时长；（5）给出了非等长时隙条件下动态帧时隙ALOHA防冲突协议。
[Abstract]:Radio frequency identification (RFID,Radio Frequency Identification) technology is a contactless automatic identification technology. As one of the key technologies of Internet of things (IoT,Internet of Things), RFID technology is widely used in the field of data acquisition and commodity identification. Today, with the rapid growth of the variety and quantity of goods in the logistics supply chain, it is urgent for readers to identify a large number of tags in the recognizable range of readers efficiently in a short period of time. This also leads to the conflict of label reading, which leads to the inefficiency of system recognition. However, due to many reasons, the capture effect will be caused, such as the distance between the tag and the reader in the system, and how to use the capture effect to improve the system recognition efficiency has become the most popular research topic in the field of RFID. In this paper, based on the dynamic frame slot ALOHA protocol (DFSA, Dynamic Framed SlottedALOHA), the algorithm of anti-collision label estimation for passive RFID systems with capture effect is systematically studied. In this paper, we propose: (1) an anti-collision label number estimation algorithm considering capture effect: 2-DBayes algorithm, which observes and counts space-time slot, successful time slot and conflict slot number, estimates the number of tags to be identified by two-dimensional maximization posterior probability search and the probability value of capture effect of the current system; (2) A label number estimation algorithm (: CAE (Capture-awareEstimation), which considers the low computational complexity under the acquisition effect, which estimates the number of tags in the system by observing the number of voids, and then estimates the probability value of the acquisition effect by the estimated number of tags. (3): ECAE (Enhanced Capture-awareEstimation) algorithm, which solves the problem that the traditional algorithm fails when the space-time slot number is zero. The algorithm asks the label to be identified by bit-slot method, and extends the frame by setting the minimum probability event threshold to make the space-time slot number not zero quickly and effectively, which ensures the effectiveness of the CAE algorithm. (4) A linear optimal frame length setting scheme under the condition of non-equal length time slot, which is compatible with ISO18000-6c standard and EPC C1Gen2 standard, that is, each time slot is not equal time length; (5) the dynamic frame slot ALOHA anti-collision protocol under the condition of non-equal length time slot is given.
【学位授予单位】：云南民族大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP391.44

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