ILOS: A Data Collection Tool and Open Datasets for Fingerprint-based Indoor Localization

被引：3
|
作者：
Cooke, Mitchell [1 ]
Wei, Yongyong [1 ]
Hao, Yujiao [1 ]
Zheng, Rong [1 ]
机构：
[1] McMaster Univ, 1280 Main St W, Hamilton, ON, Canada
来源：
PROCEEDINGS OF THE FIRST WORKSHOP ON DATA ACQUISITION TO ANALYSIS (DATA '18) | 2018年
关键词：
Indoor Localization; Location Fingerprint;
D O I：
10.1145/3277868.3277876
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Fingerprint based indoor localization is promising with distinctive signal readings such as Wi-Fi Received Signal Strength (RSS) and magnetic field in indoor environments. However, collecting location fingerprints is a time consuming process. In this paper, we present an efficient tool to collect location dependent data by utilizing inertial sensors on a smart phone. An empirical study shows that with this tool, location fingerprints can be quickly collected and an average localization accuracy around three meters can be achieved using Wi-Fi fingerprints only.
引用
收藏
页码：15 / 16
页数：2
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