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Micromachines 2015, 6(4), 523-543; doi:10.3390/mi6040523

Extended Kalman Filter for Real Time Indoor Localization by Fusing WiFi and Smartphone Inertial Sensors

1
School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
2
Nanjing Research Institute of Electronics Technology, Nanjing 210039, China
*
Author to whom correspondence should be addressed.
Academic Editor: Aboelmagd Noureldin
Received: 12 March 2015 / Revised: 11 April 2015 / Accepted: 20 April 2015 / Published: 22 April 2015
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
View Full-Text   |   Download PDF [1707 KB, uploaded 22 April 2015]   |  

Abstract

Indoor localization systems using WiFi received signal strength (RSS) or pedestrian dead reckoning (PDR) both have their limitations, such as the RSS fluctuation and the accumulative error of PDR. To exploit their complementary strengths, most existing approaches fuse both systems by a particle filter. However, the particle filter is unsuitable for real time localization on resource-limited smartphones, since it is rather time-consuming and computationally expensive. On the other hand, the light computation fusion approaches including Kalman filter and its variants are inapplicable, since an explicit RSS-location measurement equation and the related noise statistics are unavailable. This paper proposes a novel data fusion framework by using an extended Kalman filter (EKF) to integrate WiFi localization with PDR. To make EKF applicable, we develop a measurement model based on kernel density estimation, which enables accurate WiFi localization and adaptive measurement noise statistics estimation. For the PDR system, we design another EKF based on quaternions for heading estimation by fusing gyroscopes and accelerometers. Experimental results show that the proposed EKF based data fusion approach achieves significant localization accuracy improvement over using WiFi localization or PDR systems alone. Compared with a particle filter, the proposed approach achieves comparable localization accuracy, while it incurs much less computational complexity. View Full-Text
Keywords: indoor localization; WiFi; inertial sensor; extended Kalman filter indoor localization; WiFi; inertial sensor; extended Kalman filter
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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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Deng, Z.-A.; Hu, Y.; Yu, J.; Na, Z. Extended Kalman Filter for Real Time Indoor Localization by Fusing WiFi and Smartphone Inertial Sensors. Micromachines 2015, 6, 523-543.

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