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Title: Fusion of WiFi, smartphone sensors and landmarks using the Kalman filter for indoor localization
Authors: Chen, Zhenghua
Zou, Han
Jiang, Hao
Zhu, Qingchang
Soh, Yeng
Xie, Lihua
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems
Issue Date: 2015
Source: Chen, Z., Zou, H., Jiang, H., Zhu, Q., Soh, Y., & Xie, L. (2015). Fusion of WiFi, smartphone sensors and landmarks using the Kalman filter for indoor localization. Sensors, 15(1), 715-732.
Series/Report no.: Sensors
Abstract: Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.
ISSN: 1424-8220
DOI: 10.3390/s150100715
Rights: © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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