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

页码：15 / 16

页数：2