Abstract
Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning) real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement (straight or turning), which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem. Wi-Fi fingerprint matching typically requires a quite high computational burden: To reduce the computational complexity of this step, the affinity propagation clustering algorithm is adopted to cluster the fingerprint database and integrate the information of the position domain and signal domain of respective points. An experiment performed in a fourth-floor corridor at the School of Environment and Spatial Informatics, China University of Mining and Technology, shows that the traverse points of the clustered positioning system decrease by 65%–80%, which greatly improves the time efficiency. In terms of positioning accuracy, the average error is 4.09 m through the Wi-Fi positioning method. However, the positioning error can be reduced to 2.32 m after integration of the PDR algorithm with the adaptive noise extended Kalman filter (EKF).
Highlights
Indoor navigation has become an essential technology in a number of applications, such as in a supermarket as a shopping guide, a fire emergency service for navigation, or a hospital patient for tracking
This paper proposes a novel data fusion framework by using an adaptive extended Kalman filter (EKF) to integrate Wi-Fi localization with PDR
A positioning model of dead reckoning and Wi-Fi fusion is established. This scheme can adaptively determine the filtering system dynamic noise to conduct the EKF fusion calculation according to the pedestrian movement, which can improve the stability of the wireless positioning while restraining the error accumulation caused by dead reckoning
Summary
Indoor navigation has become an essential technology in a number of applications, such as in a supermarket as a shopping guide, a fire emergency service for navigation, or a hospital patient for tracking. Masiero et al proposed a particle filtering method based on the integration of information provided by the inertial navigation system measurements, the radio signal strength of a standard wireless network, and the geometrical information of the building [18]. The fingerprint-based Wi-Fi indoor positioning method includes offline sampling and real-time matching and positioning. Because the wireless signal is blocked by obstacles and disturbed by the multipath effect and other factors, positioning failure or instability phenomena such as location jumping or clustering may occur during the positioning; the dead-reckoning system based on the inertial measurement unit can achieve relatively higher precision position calculation, but the positioning error will rapidly increase with increasing walking distance. This scheme can adaptively determine the filtering system dynamic noise to conduct the EKF fusion calculation according to the pedestrian movement (straight or turning), which can improve the stability of the wireless positioning while restraining the error accumulation caused by dead reckoning
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
