Robust WiFi Localization by Fusing Derivative Fingerprints of RSS and Multiple Classifiers

It is notable that localization accuracy using received signal strength (RSS) fingerprints solely is very vulnerable to dynamic environments. Utilizing multiple fingerprints gleaned from RSS for localization is a propitious strategy to overcome the RSS susceptibility. Brimful utilization via fusing...

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Published in:	IEEE transactions on industrial informatics Vol. 16; no. 5; pp. 3177 - 3186
Main Authors:	Guo, Xiansheng, Elikplim, Nkrow Raphael, Ansari, Nirwan, Li, Lin, Wang, Lei
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.05.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Classifiers Fingerprint recognition Fingerprints indoor localization Localization Machine learning Machine learning algorithms Optimization Position measurement received signal strength (RSS) Robustness Signal strength Training two-layer fusion profile WiFi Wireless fidelity
ISSN:	1551-3203, 1941-0050
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Abstract	It is notable that localization accuracy using received signal strength (RSS) fingerprints solely is very vulnerable to dynamic environments. Utilizing multiple fingerprints gleaned from RSS for localization is a propitious strategy to overcome the RSS susceptibility. Brimful utilization via fusing multiple fingerprint functions which supplement each other are not harnessed by existing fusion-based techniques, resulting in low localization accuracy. This paper presents a novel and robust WiFi localization modus operandi by fusing DerIvative Fingerprints of RSS with MultIple Classifiers (DIFMIC). DIFMIC first constructs a multiple fingerprints group by gleaning hyperbolic location fingerprint (HLF) and signal strength differences fingerprint (DIFF) from RSS fingerprints. Then, it obtains Multiple Fingerprints Trained Classifiers (MFTCs) via training each basic classifier with each fingerprint. To fully leverage the inherent supplementation among fingerprints and classifiers, a two-layer fusion profile (weights) joint optimization algorithm with multiple constraints is proposed. We also propose a Fusion Profile Selection (FPS) algorithm to intelligently choose fusion weights from the two-layer fusion profile for a more accurate localization. DIFMIC shows more leverage in combining multiple information, thus exhibiting better robustness in WiFi positioning. Results from our experiments reflect that DIFMIC performs better than other existing methods in real environments.
AbstractList	It is notable that localization accuracy using received signal strength (RSS) fingerprints solely is very vulnerable to dynamic environments. Utilizing multiple fingerprints gleaned from RSS for localization is a propitious strategy to overcome the RSS susceptibility. Brimful utilization via fusing multiple fingerprint functions which supplement each other are not harnessed by existing fusion-based techniques, resulting in low localization accuracy. This paper presents a novel and robust WiFi localization modus operandi by fusing DerIvative Fingerprints of RSS with MultIple Classifiers (DIFMIC). DIFMIC first constructs a multiple fingerprints group by gleaning hyperbolic location fingerprint (HLF) and signal strength differences fingerprint (DIFF) from RSS fingerprints. Then, it obtains Multiple Fingerprints Trained Classifiers (MFTCs) via training each basic classifier with each fingerprint. To fully leverage the inherent supplementation among fingerprints and classifiers, a two-layer fusion profile (weights) joint optimization algorithm with multiple constraints is proposed. We also propose a Fusion Profile Selection (FPS) algorithm to intelligently choose fusion weights from the two-layer fusion profile for a more accurate localization. DIFMIC shows more leverage in combining multiple information, thus exhibiting better robustness in WiFi positioning. Results from our experiments reflect that DIFMIC performs better than other existing methods in real environments.
Author	Elikplim, Nkrow Raphael Ansari, Nirwan Wang, Lei Li, Lin Guo, Xiansheng
Author_xml	– sequence: 1 givenname: Xiansheng orcidid: 0000-0002-8440-1607 surname: Guo fullname: Guo, Xiansheng email: xsguo@uestc.edu.cn organization: Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 2 givenname: Nkrow Raphael orcidid: 0000-0003-0223-943X surname: Elikplim fullname: Elikplim, Nkrow Raphael email: nkrowraph@gmail.com organization: Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 3 givenname: Nirwan orcidid: 0000-0001-8541-3565 surname: Ansari fullname: Ansari, Nirwan email: nirwan.ansari@njit.edu organization: Department of Electrical and Computer Engineering, Advanced Networking Laboratory, New Jersey Institute of Technology, Newark, NJ, USA – sequence: 4 givenname: Lin orcidid: 0000-0002-8383-7468 surname: Li fullname: Li, Lin email: linli9419@gmail.com organization: Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 5 givenname: Lei orcidid: 0000-0002-0979-9520 surname: Wang fullname: Wang, Lei email: louiewang@foxmail.com organization: Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China
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Snippet	It is notable that localization accuracy using received signal strength (RSS) fingerprints solely is very vulnerable to dynamic environments. Utilizing...
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SubjectTerms	Algorithms Classifiers Fingerprint recognition Fingerprints indoor localization Localization Machine learning Machine learning algorithms Optimization Position measurement received signal strength (RSS) Robustness Signal strength Training two-layer fusion profile WiFi Wireless fidelity
Title	Robust WiFi Localization by Fusing Derivative Fingerprints of RSS and Multiple Classifiers
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