基于无线信号强度的RFID定位算法研究与应用

发布时间：2018-06-27 06:53

本文选题：RFID + 阅读器　；参考：《湖北工业大学》2017年硕士论文

【摘要】：射频识别(Radio Frequency Identification,RFID)是一种利用射频信号达到非接触的自动识别目标对象的技术,通过此技术不仅可以方便地获取存储在目标对象中的信息外,还可以对目标对象进行跟踪定位。RFID技术与快速发展的互联网、多媒体、物联网产业相结合,在物流领域、交通运输领域、医疗行业都有应用。因为物联网的快速发展,除了需要使用全球定位系统(GPS)对交通运输中物品进行跟踪外,还需要对存储在仓库中的物品进行货物的出库、入库和定位。在室内环境中,GPS系统无法满足室内定位的需要,而使用RFID技术则对物品进行定位成为近几年的研究热点。本论文主要针对几种基于无线信号强度的RFID室内定位算法进行了系统研究,对以下几个方面进行了研究和探讨:首先调研了射频识别系统的硬件组成和工作原理,对当前射频识别技术的研究方向和发展趋势进行了分析。其次对各种RFID定位技术,如基于无线信号到达时间定位技术、基于信号到达角度定位技术和基于无线信号强度定位技术等进行了研究,得出基于无线信号强度的定位技术更加适合市场的需求,然后对基于无线信号强度技术的各种定位算法进行了详细说明。然后使用MATLAB软件对三边测量法、三边质心法、基于参考标签的权重算法仿真实验,对每种定位算法的实验仿真结果分析了优缺点。最后提出使用粒子群算法进行室内定位,对基于参考标签的粒子群算法进行了仿真实验,认为基于无线信号强度的室内定位使用粒子群算法较为合适。粒子群算法的优势是不用额外添加昂贵的实验硬件设备的情况下,使用价格相对低廉的RFID电子参考标签作为基准,不仅可以提高定位的准确度,还使得基于参考标签的室内定位算法的稳定性得到提高,粒子群算法的主要思想是建立一种逐步优化的数学模型,使用测量得到的无线信号强度数据转换成待定位电子标签与估算位置之间的距离,逐步缩小待定位电子标签与估算位置之间的距离,最后得到一个最优解。最后利用实验室的设备实现了基于参考标签的粒子群算法定位实验,实验结果符合预期。
[Abstract]:Radio Frequency Identification (RFID) is a non-contact automatic object identification technology, which can not only obtain the information stored in the target object conveniently. The technology of RFID can also be used in the field of logistics, transportation and medical industry, combining with the rapid development of Internet, multimedia and Internet of things industry. Due to the rapid development of the Internet of things, it is necessary to use GPS to track the goods in transportation, but also to export, enter and locate the goods stored in the warehouse. In the indoor environment, GPS system can not meet the needs of indoor positioning, and RFID technology has become a research hotspot in recent years. In this paper, several RFID indoor localization algorithms based on wireless signal strength are studied systematically. The following aspects are studied and discussed: firstly, the hardware composition and working principle of RFID system are investigated. The research direction and development trend of RFID technology are analyzed. Secondly, various RFID localization technologies, such as wireless signal arrival time location, signal arrival angle location and wireless signal intensity localization, are studied. It is concluded that the localization technology based on wireless signal intensity is more suitable for the market demand, and then various localization algorithms based on wireless signal intensity technology are explained in detail. Then the MATLAB software is used to measure the triangulation method, the centroid method, the weight algorithm based on reference label simulation experiment, and the advantages and disadvantages of each localization algorithm are analyzed. Finally, the particle swarm optimization (PSO) algorithm is proposed for indoor localization. The PSO algorithm based on reference label is simulated, and it is considered that the PSO algorithm based on wireless signal intensity is more suitable for indoor localization. The advantage of particle swarm optimization is that it can not only improve the accuracy of location, but also use the relatively cheap RFID electronic reference tag as the benchmark without adding expensive experimental hardware. It also improves the stability of indoor localization algorithm based on reference label. The main idea of particle swarm optimization is to establish a mathematical model of stepwise optimization. The wireless signal strength data obtained from the measurement are converted into the distance between the tag and the estimated position, and the distance between the tag and the estimated position is gradually reduced, and an optimal solution is obtained. Finally, the experiment of particle swarm optimization based on reference label is carried out by using laboratory equipment, and the experimental results are in line with expectations.
【学位授予单位】：湖北工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.44

【参考文献】