%0 Journal Article
%T 基于层次聚类的WiFi室内位置指纹定位算法<br>WiFi indoor position fingerprinting localization algorithm based on hierarchical clustering
%A 王怡婷
%A 郭
%A 红
%J 福州大学学报(自然科学版)
%D 2017
%R 10.7631/issn.1000-2243.2017.01.0008
%X 提出一种利用WiFi信号指纹实现对室内区域进行定位的CL-KNN（complete linkage K-nearest neighbor）算法. 该算法先采用层次聚类方法对测试环境进行区域划分，再根据相应的WiFi信号指纹信息进行匹配，最后通过加权计算确定定位结果. 实验结果表明，在WiFi热点数量足够多的情况下，与原始KNN算法和k-means-KNN算法相比，CL-KNN算法可以获得更高的定位精度和准确率.<br>This paper presents a kind of using WiFi signal fingerprint to locate the interior regions of CL-KNN (complete linkage K-nearest neighbor) algorithm. The algorithm first performs hierarchical clustering method to divide a test environment to several regions，and according to the WiFi signal fingerprint information corresponding to match，finally the location results determined by weighted calculation. The experimental results show that，under the condition of the WiFi access points quantity enough，compared with the original KNN algorithm and k-means-KNN algorithm，CL-KNN algorithm can obtain higher positioning precision and accuracy
%K 室内定位 位置指纹定位 层次聚类算法 WiFi 信号&lt
%K br&gt
%K indoor positioning position fingerprint orientation hierarchical clustering algorithm WiFi signal
%U http://xbzrb.fzu.edu.cn/ch/reader/view_abstract.aspx?file_no=201701002&flag=1