巢湖学院学报 ›› 2019, Vol. 21 ›› Issue (6): 116-126.doi: 10.12152/j.issn.1672-2868.2019.06.017

• 工程与技术 • 上一篇    下一篇

基于嵌入式平台与kNN算法的室内定位研究

安徽师范大学皖江学院  电子工程系,安徽  芜湖  241000   

  1. 安徽师范大学皖江学院电子工程系
  • 收稿日期:2019-09-20 出版日期:2019-11-25 发布日期:2020-03-13
  • 通讯作者: 张辉(1984-),男,江苏泰兴人,安徽师范大学皖江学院副教授,主要从事单片机与嵌入式研究。
  • 作者简介:张辉(1984-),男,江苏泰兴人,安徽师范大学皖江学院副教授,主要从事单片机与嵌入式研究。
  • 基金资助:
    安徽省大学生创新创业训练计划项目(项目编号:201813617016)

Indoor Positioning Research Based on Embedded Platform and kNN Algorithm

Electronic Engineering Department, Wanjiang College of Anhui Normal University, Wuhu Anhui 241000   

  1. Electronic Engineering Department, Wanjiang College of Anhui Normal University
  • Received:2019-09-20 Online:2019-11-25 Published:2020-03-13
  • Contact: ZHANG Hui:Electronic Engineering Department, Wanjiang College of Anhui Normal University
  • About author:ZHANG Hui:Electronic Engineering Department, Wanjiang College of Anhui Normal University
  • Supported by:
    201813617016

摘要: 提出了一种基于嵌入式平台与kNN(k-Nearest Neighbour)算法的室内定位研究,对实验环境中的模型进行定位分析。首先,通过RFID (Radio Frequency Identification)阅读器采集RSSI(Received Signal StrengthIndication)并将数据发送到嵌入式平台,接收到数值后,利用kNN算法确定目标位置,能够实现较高精确度的定位。算法代码采用Python语言,硬件平台采用ARM构架,该方法具有体积小、代码简洁、方便移植等优点。

关键词: 嵌入式平台, kNN, 室内定位算法, Python

Abstract: This paper proposes an indoor positioning study based on embedded platform and kNN algorithm, and locates the model in the experimental environment. First, the RFID (Radio Frequency Identification)reader collects RSSI (Received Signal Strength Indication)and sends the data to the embedded platform. After receiving the value,the kNN (k-Nearest Neighbor)algorithm is used to determine the target position, which can achieve higher accuracy positioning. The algorithm code uses Python language, and the hardware platform adopts ARM architecture.
This method has the advantages of small size, simple code and convenient porting.

Key words: embedded platform, kNN, indoor positioning algorithm, Python

中图分类号: 

  • TP391