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Open AccessArticle

Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

1
Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China
2
Career Science Lab, BOSS Zhipin, Beijing 10028, China
3
Department of Electrical and Computer Engineering, University of Canterbury, Christchurch 8041, New Zealand
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 1067; https://doi.org/10.3390/s20041067
Received: 7 January 2020 / Revised: 9 February 2020 / Accepted: 13 February 2020 / Published: 15 February 2020
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods. View Full-Text
Keywords: indoor localization; fingerprint positioning; received signal strength; hierarchical classification indoor localization; fingerprint positioning; received signal strength; hierarchical classification
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Zhang, C.; Qin, N.; Xue, Y.; Yang, L. Received Signal Strength-Based Indoor Localization Using Hierarchical Classification. Sensors 2020, 20, 1067.

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