基于改进指纹聚类的WLAN定位优化方法

将K-means聚类算法应用到无线局域网（WLAN）位置指纹定位中，虽然可以缩短定位时间，但是容易降低定位精度。为了解决此问题，提出了基于改进指纹聚类的WLAN定位优化方法。首先根据接收信号强度标准差来优化初始聚类中心的选取,然后对指纹数据进行聚类处理,最后进行在线定位。实验结果表明，与传统的WLAN位置指纹定位方法和K-means聚类定位方法相比，基于改进指纹聚类的定位优化方法不仅缩短了定位时间，还能有效提高定位精度。

An optimization method of WLAN positioning based on improved fingerprint clustering

Although the application of K-means clustering algorithm in the Wireless Local Area Network(WLAN) fingerprint positioning can shorten the positioning time,it is easy to reduce the positioning accuracy.In order to solve this problem,an optimization method of WLAN positioning based on improved fingerprint clustering is proposed.Firstly,the selection of the initial clustering center is optimized according to the standard deviation of the received signal strength,then the position fingerprint data is clustered.Finally,the online positioning is carried out.The experiment results show that compared with the traditional WLAN fingerprint positioning method and the K-means clustering positioning method,the proposed method based on improved fingerprint clustering not only reduces the location time,but also improves the location accuracy effectively.