Real-time Indoor Localization System

The past decade has witnessed the conceptualization and development of various wireless indoor localization techniques, including WiFi, RFID, acoustic signals, etc. Due to the wide deployment and availability of WiFi infrastructure, WiFi fingerprint-based indoor localization has become one of the most attractive techniques for ubiquitous applications.

The basic idea of fingerprint-based indoor localization technique is to take advantage of the spatial diversity of wireless signals at different positions. By collecting WiFi signal characteristics (typically, the RSS) as the location fingerprint and establishing a fingerprint-location relational database, the system can localize the user using certain fingerprint matching algorithm.

The general framework can be divided into two phases: training and operating. The former involves a site survey process (a.k.a. calibration), in which RSSs from multiple APs at every location of interest are measured and recorded as WiFi fingerprints. Accordingly a fingerprint database (a.k.a. radio map) is built, where each location is labelled with its corresponding fingerprints. In the operating stage, to locate a user sends a query with his current fingerprint, localization server retrieves the fingerprint database and return the location of the best-matched fingerprints as the user’s location estimation.

Despite of its success, WiFi positioning faces several challenges on its fast track of practical development. In this project, we aim to design novel techniques to pave the way for WiFi fingerprint based localization as a ubiquitous, long-term and accurate service. Particularly, we focus on exploring solutions to enable automatic radio map construction, adaptive radio map updating, and robust location estimation, etc.

We deployed a wireless localization system, PosX, to implement, evaluate, and validate our research innovations. The system has been deployed at Tsinghua University, Tsinghua Wuxi IOT Center, and West Anhui University. PosX mainly features in the following perspectives:

  • Integration and comparison of multiple real-time localization algorithms, including both classical ones and our novel designs.
  • Fusion of both wireless signal characteristics and user’s mobility information.
  • Capability of automatic AP location estimation
  • Visualization of WiFi fingerprints and radio maps


PosX Screenshots

Footprint database in server-side

Hotspots in server-side

Real-time wireless information in server-side

Localization in Server-side

Localization in Mobile-side

Motion in Mobile Side

Books & Publications

Location, Localization, and Localizability - Location-awareness Technology for Wireless Networks

by Yunhao Liu and Zheng Yang

1st Edition, Springer, Berlin, 2011.

ISBN: 978-1-4419-7370-2

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Location-based Computing: Localization and Localizability of Wireless Networks

by Zheng Yang, Chenshu Wu and Yunhao Liu

1st Edition, Tsinghua University Press, Beijing, 2014.

ISBN: 978-7-302-35113-9

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  1. Zheng Yang, Chenshu Wu, Zimu Zhou, Xinglin Zhang, Xu Wang, Yunhao Liu, Mobility Increases Localizability: A Survey on Wireless Indoor Localization using Inertial Sensors, ACM Computing Surveys, Volume 47, Issue 3, Article No. 54, 2015.
  2. Chenshu Wu, Zheng Yang, Yunhao Liu, Smartphones based Crowdsourcing for Indoor Localization, IEEE Transactions on Mobile Computing (TMC), Volume 14, Issue 2, 444 – 457, February 2015.
  3. Chenshu Wu, Zheng Yang, Chaowei Xiao, Chaofan Yang, Yunhao Liu, Mingyan Liu, Static Power of Mobile Devices: Self-updating Radio Maps for Wireless Indoor Localization, IEEE INFOCOM 2015, Hong Kong, April 26 – May 1, 2015.
  4. Xinglin Zhang, Zheng Yang, Chenshu Wu, Wei Sun, Yunhao Liu, and Kai Xing, Robust Trajectory Estimation for Crowdsourcing-based Mobile Applications, IEEE Transactions on Parallel and Distributed Computing (TPDS), Volume 25, Issue 7, Pages 1876 – 1885, July 2014.
  5. Chenshu Wu, Zheng Yang, Yunhao Liu, and Wei Xi, WILL: Wireless Indoor Localization without Site Survey, IEEE Transactions on Parallel and Distributed Systems (TPDS), Volume 24, Issue 4, Pages 839-848, April, 2013.
  6. Zheng Yang, Chenshu Wu, and Yunhao Liu, Locating in Fingerprint Space: Wireless Indoor Localization with Little Human Intervention, ACM MobiCom 2012, Istanbul, Turkey, August 22-26, 2012.