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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Bibliographic Details
Published in:IEEE internet of things journal 2018-12, Vol.5 (6), p.4686-4698
Main Authors: Guo, Xiansheng, Ansari, Nirwan, Li, Lin, Li, Huiyong
Format: Article
Language:English
Subjects:
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
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Summary: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 based on some single fingerprints is rather susceptible to the changing environment. The efficiency of building single fingerprints from one localization system is also low. Recently, we first proposed a group of fingerprints (GOOF) based localization to improve the efficiency of building fingerprints, and then proposed an efficient fusion algorithm, namely, multiple classifiers multiple samples (MUCUS), to improve the accuracy of localization. However, the main drawbacks of MUCUS are the low localization efficiency and low accuracy when all classifiers show poor performance simultaneously. In this paper, based on the aforementioned GOOF, we propose a sliding window aided mode-based (SWIM) fusion algorithm to balance the localization accuracy and efficiency. SWIM first adopts windowing and sliding techniques to improve the localization efficiency, and then obtains a more accurate estimate by minimizing the entropy of multiple classifiers or multiple samples. This can guarantee our estimator to be robust to changing environment and larger noise level. We demonstrate the performance of our algorithms through simulations and real experimental data via two universal software radio peripheral platforms.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2018.2810601