WiFi/PDR-integrated indoor localization using unconstrained smartphones

Abstract In this paper, we propose a WiFi/pedestrian dead reckoning (PDR)-integrated localization approach based on unscented Kalman filters (UKF). The UKF integrating WiFi localization with PDR is used for ultimate location estimation. Instead of setting process and measurement noise-related parameters empirically as previous works, the error covariance of user heading estimation in PDR state model can be accurately estimated by developing another UKF, while the measurement noise statistics in WiFi localization are estimated by deploying a kernel density estimation-based model. Another developed UKF is used for device attitude tracking in user heading estimation of PDR. Besides, in order to adapt the unconstrained carrying positions and orientations of smartphones, we propose a robust carrying position recognition method based on orientation invariant features. Experimental results show that the proposed WiFi/PDR-integrated localization approach may improve traditional approaches in terms of reliability and localization accuracy