A Comprehensive Study of Bluetooth Fingerprinting-based Algorithms For Localization

被引：37
|
作者：
Zhang, Li [1 ]
Liu, Xiao [1 ]
Song, Jie [1 ]
Gurrin, Cathal [2 ]
Zhu, Zhiliang [1 ]
机构：
[1] Northeastern Univ, Software Coll, Shenyang, Peoples R China
[2] Dublin City Univ, Sch Comp, Dublin 9, Ireland
来源：
2013 IEEE 27TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS WORKSHOPS (WAINA) | 2013年
基金：
中国国家自然科学基金;
关键词：
Bluetooth indoor positioning; Fingerprinting; kNN; Neural Networks; SVM;
D O I：
10.1109/WAINA.2013.205
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
There is an increasing demand for indoor navigation and localization systems along with the increasing popularity of location based services in recent years. According to past researches, Bluetooth is a promising technology for indoor wireless positioning due to its cost-effectiveness and easy-to-deploy feature. This paper studied three typical fingerprinting-based positioning algorithms - kNN, Neural Networks and SVM. According to our analysis and experimental results, the kNN regression method is proven to be a good candidate for localization in real-life application. Comprehensive performance comparisons including accuracy, precision and training time are presented.
引用
收藏
页码：300 / 305
页数：6
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