基于RFID技术的室内定位系统的研究

发布时间：2018-11-12 12:17

【摘要】：随着移动通信和无线技术的快速发展,室内无线定位技术的需要越来越多,应用也越来越广泛。在室外定位方面,GPS等全球卫星定位系统及无线蜂窝定位系统已经广泛运用在工作生活的各个方面。在室内定位系统中,有Zig Bee定位技术、超声波定位技术、红外线定位技术、蓝牙定位技术、射频识别(RFID)定位技术及Wi-Fi定位技术等,但由于室内环境比较复杂,多径效应影响严重,这些技术中大多数的定位效果不是很好。相比之下,射频识别(RFID)技术同时具有成本低,非视距,非接触等优点,并能结合互联网通信技术,实现全球范围内的物品跟踪识别与信息共享。本文首先对RFID室内定位系统相关的RFID技术及TOA、RSSI、TDOA等定位算法进行了概述,然后以RFID室内定位的经典算法LANDMARC为基础,分析其工作原理、性能特点以及可能影响其定位精度的各种因素,并对该算法进行了探索性研究。文章就两个方面对LANDMARC算法进行了改进。第一,对定位区域进行网格化。对定位区域内的参考标签进行网格化处理,即对每个参考标签之间的RSSI值进行更细化的处理,从而在不增加参考标签的情况下能更精确的确定待测标签的位置。第二,对待测标签的邻接参考标签的选择进行了改进。改进算法采用类聚的思想,选择与待测标签RSSI值最接近的参考标签为邻接标签,这样可以忽略阅读器到标签之间的各种影响,从而更能准确的显示待测标签的实际位置。最后对该改进算法进行了Matlab的仿真。仿真结果表明,改进后的算法在绝大多数情况下误差要小于原LANDMARC算法,平均定位精度相对原算法提高了近3倍。
[Abstract]:With the rapid development of mobile communication and wireless technology, indoor wireless positioning technology needs more and more widely. In outdoor positioning, GPS and wireless cellular positioning systems have been widely used in all aspects of working life. In the indoor positioning system, there are Zig Bee positioning technology, ultrasonic positioning technology, infrared positioning technology, Bluetooth positioning technology, radio frequency identification (RFID) positioning technology and Wi-Fi positioning technology, etc. The multipath effect is very serious, and most of these techniques are not very good. In contrast, RFID (RFID) technology has the advantages of low cost, non-visual distance, non-contact, and can combine with Internet communication technology to achieve global object tracking identification and information sharing. In this paper, the RFID technology related to RFID indoor positioning system and TOA,RSSI,TDOA localization algorithms are summarized, and then based on the classical RFID indoor positioning algorithm LANDMARC, its working principle is analyzed. The performance characteristics and various factors that may affect the positioning accuracy are discussed. This paper improves the LANDMARC algorithm in two aspects. First, gridding the location area. The reference label in the location area is meshed, that is, the RSSI value between each reference label is processed more finely, so that the location of the tag can be determined more accurately without adding the reference label. Secondly, the selection of adjacent reference labels for test labels is improved. The improved algorithm adopts the idea of clustering and selects the reference label which is closest to the RSSI value of the tag to be tested as the adjacent tag so that the influence between the reader and the tag can be ignored and the actual position of the tag to be tested can be more accurately displayed. Finally, the improved algorithm is simulated by Matlab. The simulation results show that the error of the improved algorithm is lower than that of the original LANDMARC algorithm in most cases, and the average positioning accuracy is nearly three times higher than that of the original algorithm.
【学位授予单位】：南昌大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.44

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