Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests

Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, autonomous parking, virtual reality, context awareness, condition monitoring, geolocation, smart manufacturing, as well as smart cities. It is well known that indoor localization...

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Veröffentlicht in:	IEEE internet of things journal Jg. 5; H. 6; S. 4686 - 4698
Hauptverfasser:	Guo, Xiansheng, Ansari, Nirwan, Li, Lin, Li, Huiyong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.12.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Algorithms Changing environments Classifiers Computer simulation Condition monitoring Efficiency Fingerprint identification Fingerprints Group of fingerprints (GOOFs) Indoor environments Internet of Things Localization Location awareness Mucus multiple antennas random forests (RFs) Sliding sliding window aided mode-based (SWIM) fusion Software radio universal software radio peripheral (USRP) Virtual reality
ISSN:	2327-4662, 2327-4662
Online-Zugang:	Volltext
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