A comprehensive study of bluetooth fingerprinting-based algorithms for localization
Zhang, Li, Liu, Xiao, Song, Jie, Gurrin, CathalORCID: 0000-0003-4395-7702 and Zhu, Zhiliang
(2013)
A comprehensive study of bluetooth fingerprinting-based algorithms for localization.
In: 2013 27th International Conference on Advanced Information Networking and Applications Workshops, 25-28 Mar 2013, Barcelona, Spain.
ISBN 978-0-7695-4952-1
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.
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Bluetooth indoor positioning; Fingerprinting; kNN; Neural Networks; SVM
This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:
National Natural Science Foundation of China under Grant No. 61173028, Natural Science Foundation of Liaoning Province under Grant No.200102059, Fundamental Research Funds for the Central Universities N110417002
ID Code:
25542
Deposited On:
23 Feb 2021 11:57 by
Michael Scriney
. Last Modified 16 Dec 2022 10:41