Indoor positioning based on tightly coupling of PDR and one single Wi-Fi FTM AP

ABSTRACT

The indoor positioning system is now an important technique as part of the Internet-of-Things (IoT) ecosystem. Among indoor positioning techniques, multiple Wi-Fi Access Points (APs)-based positioning systems have been researched a lot. There is a lack of research focusing on the scene where only one Wi-Fi AP is available. This work proposes a hybrid indoor positioning system that takes advantage of the Fine-Timing Measurements (FTM) technique that is part of the IEEE 802.11mc standard, introduced back in 2016. The system uses one single Wi-Fi FTM AP and takes advantage of the built-in inertial sensors of the smartphone to estimate the device’s position. We explore both Loosely Coupled (LC) and Tightly Coupled (TC) integration schemes for the sensors’ data fusion. Experimental results show that the proposed methods can achieve an average positioning accuracy of about 1 m without knowing the initial position. Compared with the LC integration method, the median error accuracy of the proposed TC fusion algorithm has improved by more than 52% and 67%, respectively, in the two experiments we set up.

KEYWORDS:

• Indoor positioning
• Wi-Fi Fine Time Measurement (FTM)/Round Trip Time (RTT)
• Tightly Coupled (TC) integration
• Pedestrian Dead Reckoning (PDR)
• Extended Kalman Filter (EKF)

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data and materials that support the findings of this study are freely available upon request from the corresponding author (corresponding author’s e-mail: [email protected]).

Additional information

Funding

This work is supported by the National Key Research and Development Program of China [grant numbers 2016YFB0502200, 2016YFB0502201] and the NSFC [grant number 91638203]

Notes on contributors

Yuan Wu

Yuan Wu is pursuing his doctoral degree at Wuhan University. His research interests include indoor/outdoor seamless positioning/navigation, data-driven-based positioning perception, and multi-sensors fusion.

Ruizhi Chen

Ruizhi Chen is a professor and director of the State Key Laboratory of Surveying, Mapping and Remote Sensing Information Engineering, Wuhan University, and an academician of the Finnish Academy of Sciences and Humanities. He is an internationally renowned scholar in the field of navigation and positioning. He is committed to the theoretical research and core technology development of smartphone indoor and outdoor seamless navigation and positioning and low-orbit satellite navigation enhancement.

Wenju Fu

Wenju Fu is a Postdoc researcher at Wuhan University. His research interests include GNSS data processing and LEO orbit determination.

Wei Li

Wei Li is pursuing his doctoral degree at Wuhan University. His research interests include indoor positioning/navigation and millimeter-wave radar positioning.

Haitao Zhou

Haitao Zhou is a PhD candidate at Wuhan University. His research interests include surveying data processing and GNSS precise positioning.

Guangyi Guo

Guangyi Guo is a Postdoc researcher at Wuhan University. His research interests include indoor positioning, acoustic localization, and mobility context computing.