Indoor position technology has become one of the research highlights in the Internet of Things (IoT), but there is still a lack of universal, low-cost, and high-precision solutions. This paper conducts research on indoor position technology based on location fingerprints and proposes a practical hybrid indoor positioning system. In this experiment, the location fingerprint database is established by using RSS signal in the offline stage, the location algorithm is improved and innovated in the online stage. The weighted k-nearest neighbor algorithm is used for location fingerprint matching and pedestrian dead reckoning technology is used for trajectory tracking. This paper designs and implements an indoor position system that performs the functions of data collection, positioning, and position tracking. Through the test, it is found that it can meet the requirements of indoor positioning.

翻译：室内定位技术已成为物联网（IoT）领域的研究热点之一，但目前仍缺乏通用、低成本且高精度的解决方案。本文针对基于位置指纹的室内定位技术展开研究，提出了一种实用的混合室内定位系统。本实验在离线阶段利用接收信号强度（RSS）信号建立位置指纹数据库，在线阶段对定位算法进行改进与创新。系统采用加权k近邻算法进行位置指纹匹配，并利用行人航位推算技术进行轨迹追踪。本文设计并实现了一个具备数据采集、定位及位置追踪功能的室内定位系统。测试结果表明，该系统能够满足室内定位的需求。