WiFi/PDR-integrated indoor localization using unconstrained smartphones

Jianguo Yu,Zhian Deng,Xin Liu,Zhenyu Na

doi:10.1186/s13638-019-1365-9

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.

Highlights

Various indoor localization technologies [1,2,3,4], such as WiFi, ultra-wideband, radio frequency identification, and pedestrian dead reckoning (PDR), have been developed
This paper proposes a novel-integrated localization approach based on two unscented Kalman filters (UKF)
In order to exploit the strength of WiFi localization and avoid its weakness, we integrate PDR to improve the accuracy and reliability of WiFi localization results

Summary

Introduction

Various indoor localization technologies [1,2,3,4], such as WiFi, ultra-wideband, radio frequency identification, and pedestrian dead reckoning (PDR), have been developed. Reference [9] presents a Kalman filter-based WiFi/ PDR-integrated approach This approach assumes PDR being a linear formulation and that the user heading and walking step length are known accurately. For different device carrying positions, the walking step length estimation parameters may change due to different acceleration signal statistics These parameters can be set by offline training for each user and recognized carrying position. Substitute (30) into (19), the user heading can be given as, ψ 1⁄4 ψRMPCA þ f ðqðt1ÞÞ ð31Þ where ψRMPCA is a constant value obtained by the initial heading estimation of RMPCA and f(q(t1)) is a nonlinear function with the quaternion vector at a fixed time within the same step as input variable

KDE-based WiFi localization for measurement update

Results and discussion

Conclusions