基于位置指纹的室内定位系统的设计与实现

发布时间：2018-01-07 03:28

本文关键词：基于位置指纹的室内定位系统的设计与实现　出处：《内蒙古大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着无线通信技术的发展,Wi-Fi(WirelessFidelity)得到了极大地发展,它具有移动性、方便性、廉价性的特点。在室外环境中,基于位置的服务(Location Based Service,LBS)在各方面得到了广泛应用。而在室内环境中,由于受到周围建筑物和复杂环境的影响,传统的定位方法无法得到准确的定位,难以满足室内定位的需求。由于免费Wi-Fi热点覆盖广泛,并且具有使用方便、价格便宜的特点,因此基于Wi-Fi的室内定位技术成为了研究热点。本文研究了基于接收信号强度指示(Received Signal Strength Indicator,RSSI)位置指纹的定位算法,并分析障碍物及天线方向等的影响,提出基于划分区域的K近邻(K-Nearest Neighbor,KNN)改进算法,该算法应用了信号预处理算法、KNN加权算法和K优化算法。实际场景实验表明:改进的算法与KNN算法相比,定位精确度提高了 7%,缩短了运算时间。
[Abstract]:With the development of wireless communication technology, Wi-Fi (WirelessFidelity) has been greatly developed, it has the mobility, convenience and cheap characteristics. In the outdoor environment, location based service (Location Based, Service, LBS) has been widely used in various aspects. But in the indoor environment, influenced by the surrounding buildings and the complex environment, the traditional positioning method cannot get accurate positioning, it is difficult to meet the demand for indoor positioning. Due to the free Wi-Fi hotspot wide coverage, and has the advantages of convenient use, price is cheap, so the indoor positioning technology based on Wi-Fi has become a research hotspot. In this paper based on received signal strength indicator (Received Signal Strength Indicator RSSI), fingerprint positioning algorithm, and analyze the impact of obstacles and the antenna direction, based on the regional division of the K (K-Nearest Neighbor, KNN near neighbor The improved algorithm is based on signal preprocessing algorithm, KNN weighted algorithm and K optimization algorithm. Real scene experiments show that the location accuracy is improved by 7% and the computation time is shortened by comparing the improved algorithm with KNN algorithm.

【学位授予单位】：内蒙古大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN92

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