Analysis of K-Means Algorithm on Fingerprint Based Indoor Localization System

被引：0
|
作者：
Bai, Sidong [1 ]
Wu, Tong [1 ]
机构：
[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing, Peoples R China
来源：
2013 5TH IEEE INTERNATIONAL SYMPOSIUM ON MICROWAVE, ANTENNA, PROPAGATION AND EMC TECHNOLOGIES FOR WIRELESS COMMUNICATIONS (MAPE) | 2013年
关键词：
indoor localization system; clustering; K-Means; fingerprinting localization;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The collected fingerprints at the anchors in indoor localization system are clustered with corrected K-Means algorithm in order to reduce the computational complexity in the online localization phase. When the WLAN indoor environment contains enough access points (APs), every anchor's fingerprint may have too many different dimensions. Therefore these fingerprints should be principal component analysis (PCA) and set dimension's property dynamically when clustering. The up number limit of clusters for common fingerprint database is provided. And the optimized cluster number within the up number limit and default dimension setting are provided simultaneously.
引用
收藏
页码：44 / 48
页数：5
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