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
中图分类号:
吴泽泰,蔡仁钦,徐书燕,吴小思,傅予力. 基于K近邻法的WiFi定位研究与改进[J]. 计算机工程.
WU Zetai,CAI Renqin,XU Shuyan,WU Xiaosi,FU Yuli. Research and Improvement of WiFi Positioning Based on K Nearest Neighbor Method[J]. Computer Engineering.