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计算机工程

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基于K近邻法的WiFi定位研究与改进

吴泽泰,蔡仁钦,徐书燕,吴小思,傅予力   

  1. (华南理工大学 电子与信息学院,广州 510640)
  • 收稿日期:2016-01-15 出版日期:2017-03-15 发布日期:2017-03-15
  • 作者简介:吴泽泰(1991—),男,硕士研究生,主研方向为无线定位技术;蔡仁钦、徐书燕、吴小思,硕士研究生;傅予力,教授、博士生导师。
  • 基金资助:
    广州市科技计划项目“基于压缩感知的无线室内定位关键技术及应用”(2014J4100247)。

Research and Improvement of WiFi Positioning Based on K Nearest Neighbor Method

WU Zetai,CAI Renqin,XU Shuyan,WU Xiaosi,FU Yuli   

  1. (College of Electronics and Information,South China University of Technology,Guangzhou 510640,China)
  • Received:2016-01-15 Online:2017-03-15 Published:2017-03-15

摘要: 在分析位置指纹识别算法的基础上,研究K近邻(KNN)法在室内定位中的应用。为提高定位精度,设计新的相似度计算公式。针对K近邻法计算量大问题,将聚类算法与KNN相结合,提出一种新的WiFi定位算法。实验结果表明,该算法在WiFi定位上与KNN精度基本一致,但定位时间相应缩短,可以满足室内和室外的定位要求。

关键词: WiFi定位, 机器学习, 位置指纹识别, K近邻法, 聚类, 箱形图

Abstract: In this paper,based on fingerprinting,the application of K Nearest Neighbor(KNN) method in indoor positioning is researched.In order to improve the positioning accuracy,this paper puts forward a new formula for calculating the similarity.Aiming at the problem of large amounts of computation for KNN method,it combines the clustering algorithm and KNN method,and proposes a new positioning algotithm.Experimental results show that,compared with the KNN,the proposed algorithm has comparable accuracy,and it significantly reduces the positioning time,which can satisfy the requirements of indoor and outdoor positioning.

Key words: WiFi positioning, machine learning, location fingerprinting, K Nearest Neighbor(KNN) method, clustering, boxplot

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