An Indoor Positioning Algorithm Based on Fingerprint and Mobility Prediction in RSS Fluctuation-Prone WLANs

被引：19
作者：
Lin, Chun-Han [1 ]
Chen, Lyu-Han [2 ]
Wu, Hsiao-Kuang [1 ]
Jin, Ming-Hui [1 ]
Chen, Gen-Huey [2 ]
Gomez, Jose Luis Garcia [2 ]
Chou, Cheng-Fu [2 ]
机构：
[1] Natl Cent Univ, Dept Comp Sci & Informat Engn, Taoyuan 32001, Taiwan
[2] Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei 10617, Taiwan
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2021年 / 51卷 / 05期
关键词：
Servers; Prediction algorithms; Wireless LAN; Position measurement; Global Positioning System; Buildings; Systems architecture; Fingerprint; indoor positioning; mobility prediction; received signal strength (RSS); wireless local area network (WLAN); DIVERSITY;
D O I：
10.1109/TSMC.2019.2917955
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The creation of context-aware services in pervasive computing environments has driven the wide development of wireless local area network (WLAN)-based indoor positioning systems. One of the main challenges in WLAN-based indoor positioning is the severe fluctuation of received signal strength (RSS), which may cause the RSS patterns to be mismatched and the positioning to be inaccurate. In this paper, an indoor positioning algorithm that combines the fingerprint scheme with mobility prediction is proposed. Since the mobility prediction is performed according to the moving speed and direction of the mobile client, the resulting location estimation is more stable compared to the use of RSS alone. Experimental results show that the proposed positioning algorithm can mitigate the impact of the RSS fluctuation and has better positioning accuracy and stability than previous fingerprint-based approaches.
引用
收藏
页码：2926 / 2936
页数：11
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