Next Article in Journal
Transferable Integrated Optical SU8 Devices: From Micronic Waveguides to 1D-Nanostructures
Next Article in Special Issue
Signal Processing Technique for Combining Numerous MEMS Gyroscopes Based on Dynamic Conditional Correlation
Previous Article in Journal / Special Issue
Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors
Article Menu

Export Article

Open AccessArticle
Micromachines 2015, 6(4), 523-543;

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

School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
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)
Full-Text   |   PDF [1707 KB, uploaded 22 April 2015]   |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Micromachines EISSN 2072-666X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top