cogcom 19(14): e2

Research Article

WIFI/PDR indoor integrated positioning system in a multi-floor environment

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  • @ARTICLE{10.4108/eai.11-5-2018.155075,
        author={Mu Zhou and Maxim Dolgov and Yiyao Liu and Yanmeng Wang},
        title={WIFI/PDR indoor integrated positioning system in a multi-floor environment},
        journal={EAI Endorsed Transactions on Cognitive Communications},
        keywords={Extended Kalman Filter, Multi-Floor Positioning, Pedestrian Dead Reckoning, Wi-Fi Fingerprinting},
  • Mu Zhou
    Maxim Dolgov
    Yiyao Liu
    Yanmeng Wang
    Year: 2018
    WIFI/PDR indoor integrated positioning system in a multi-floor environment
    DOI: 10.4108/eai.11-5-2018.155075
Mu Zhou1, Maxim Dolgov1,*, Yiyao Liu1, Yanmeng Wang1
  • 1: Chongqing Key Lab Of Mobile Communications Technology, Chongqing University Of Posts And Telecommunications, Chongqing 400065, China
*Contact email:


To improve the accuracy of indoor positioning for location-based services, we created an improved WiFi/PDR integrated positioning and navigation system where we are using Extended Kalman filter (EKF). The proposed algorithm first relies on MEMS in our mobile phone to estimate the velocity and heading angles of the target. Second, the velocity and heading angles, together with the results of WiFi fingerprinting-based positioning, are considered as the input of the EKF for the sake of conducting two-dimensional positioning. Third, the proposed algorithm calculates the altitude of the target by using the real-time recorded barometer. The results of our experiments show that integrated navigation system using Extended Kalman filter can effectively eliminate the accumulated errors in the PDR positioning algorithm and can reduce the influence of the large-scale jump of the WiFi fingerprint positioning result brought by the RSSI disturbance on the positioning accuracy of the system.