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Sensors 2016, 16(12), 2100; doi:10.3390/s16122100

Wi-Fi/MARG Integration for Indoor Pedestrian Localization

Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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Author to whom correspondence should be addressed.
Academic Editor: Wendong Xiao
Received: 28 August 2016 / Revised: 23 November 2016 / Accepted: 6 December 2016 / Published: 10 December 2016
(This article belongs to the Section Sensor Networks)

Abstract

With the wide deployment of Wi-Fi networks, Wi-Fi based indoor localization systems that are deployed without any special hardware have caught significant attention and have become a currently practical technology. At the same time, the Magnetic, Angular Rate, and Gravity (MARG) sensors installed in commercial mobile devices can achieve highly-accurate localization in short time. Based on this, we design a novel indoor localization system by using built-in MARG sensors and a Wi-Fi module. The innovative contributions of this paper include the enhanced Pedestrian Dead Reckoning (PDR) and Wi-Fi localization approaches, and an Extended Kalman Particle Filter (EKPF) based fusion algorithm. A new Wi-Fi/MARG indoor localization system, including an Android based mobile client, a Web page for remote control, and a location server, is developed for real-time indoor pedestrian localization. The extensive experimental results show that the proposed system is featured with better localization performance, with the average error 0.85 m, than the one achieved by using the Wi-Fi module or MARG sensors solely. View Full-Text
Keywords: indoor pedestrian localization; Wi-Fi; MARG; PDR; EKPF indoor pedestrian localization; Wi-Fi; MARG; PDR; EKPF
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Tian, Z.; Jin, Y.; Zhou, M.; Wu, Z.; Li, Z. Wi-Fi/MARG Integration for Indoor Pedestrian Localization. Sensors 2016, 16, 2100.

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