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Open AccessArticle

Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization

1
Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada
2
Electronics and Communications Engineering Department, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Alexandria, Egypt
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper Salamah, A.H.; Tamazin, M.; Sharkas, M.A.; Khedr, M. An enhanced WiFi indoor localization system based on machine learning, in Proceedings of the 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016), Alcala de Henares, Spain, 4–7 October 2016.
Sensors 2019, 19(7), 1678; https://doi.org/10.3390/s19071678
Received: 5 March 2019 / Revised: 30 March 2019 / Accepted: 3 April 2019 / Published: 8 April 2019
(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)
The smartphone market is rapidly spreading, coupled with several services and applications. Some of these services require the knowledge of the exact location of their handsets. The Global Positioning System (GPS) suffers from accuracy deterioration and outages in indoor environments. The Wi-Fi Fingerprinting approach has been widely used in indoor positioning systems. In this paper, Principal Component Analysis (PCA) is utilized to improve the performance and to reduce the computation complexity of the Wi-Fi indoor localization systems based on a machine learning approach. The experimental setup and performance of the proposed method were tested in real indoor environments at a large-scale environment of 960 m2 to analyze the performance of different machine learning approaches. The results show that the performance of the proposed method outperforms conventional indoor localization techniques based on machine learning techniques. View Full-Text
Keywords: Wi-Fi indoor navigation; machine learning; principal component analysis Wi-Fi indoor navigation; machine learning; principal component analysis
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Salamah, A.H.; Tamazin, M.; Sharkas, M.A.; Khedr, M.; Mahmoud, M. Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization. Sensors 2019, 19, 1678.

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