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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Vydáno v:	IEEE internet of things journal Ročník 5; číslo 6; s. 4686 - 4698
Hlavní autoři:	Guo, Xiansheng, Ansari, Nirwan, Li, Lin, Li, Huiyong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.12.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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.
AbstractList	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.
Author	Ansari, Nirwan Li, Lin Guo, Xiansheng Li, Huiyong
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SubjectTerms	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
Title	Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests
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