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

页码：300 / 305

页数：6

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