基于RFID的室内定位算法分析-电子与通信工程专业论文.docx


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Abstract
At present, the IOT ( Of Things) boom is sweeping all over the world. As a result, the related technologies are developing rapidly through this precious opportunity. RFID (Radio Frequency Identification) technology is the cornerstone of the IOT. Due to its special advantages of non-contact, multi-objective, non line-of-sight, fast recognition and high efficiency, RFID has e the research focus at home and abroad.
Along with the rapid development of the mobile work and mobile , in today’s era of global village, the position information plays a significant role in the fast-paced society. At the same time, it has a huge impact on the surveillance of the IOT. The indoor positioning, as the hot spot in wireless positioning, not only changes people’s way of life, but also affects the life quality. Being regarded as the shining star, RFID is ing the most popular indoor positioning technology because of its unique characters. Therefore, the research of indoor positioning based on RFID technology has a great value in the theoretical research and practical application.
The thesis introduces the working principle of a RFID system firstly. Then it has a brief introduction of the reader and the tag, which are the ponents of the system. After that, the application examples and characteristics of RFID technology are showed; meanwhile, RFID technology development trend is forecasted. Then this thesis puts focus on the classic LANDMARC (the LocAtioN iDentification on the dynaMic Active Rfid Calibration) nearest neighbor positioning algorithm and VIRE (Virtual Reference Elimination) algorithm which are based on the received signal strength. Their principles, advantages and disadvantages are analyzed respectively. On this basis, we acquire the essence of these two classic algorithms: nearest neighbor reference tags from LANDMARC, the idea of building virtual reference tags from VIRE. However, a large amount of unnecessary calculation occurrs when regarding all the reference

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  • 页数104
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  • 文件大小1.52 MB
  • 时间2019-01-05