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Continuous Indoor Positioning Fusing WiFi, Smartphone Sensors and Landmarks


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1

School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

2

School of Electronics Engineering, Heilongjiang University, Harbin 150080, China

3

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China





*

Author to whom correspondence should be addressed.



Academic Editor: Xue-Bo Jin

Abstract To exploit the complementary strengths of WiFi positioning, pedestrian dead reckoning PDR, and landmarks, we propose a novel fusion approach based on an extended Kalman filter EKF. For WiFi positioning, unlike previous fusion approaches setting measurement noise parameters empirically, we deploy a kernel density estimation-based model to adaptively measure the related measurement noise statistics. Furthermore, a trusted area of WiFi positioning defined by fusion results of previous step and WiFi signal outlier detection are exploited to reduce computational cost and improve WiFi positioning accuracy. For PDR, we integrate a gyroscope, an accelerometer, and a magnetometer to determine the user heading based on another EKF model. To reduce accumulation error of PDR and enable continuous indoor positioning, not only the positioning results but also the heading estimations are recalibrated by indoor landmarks. Experimental results in a realistic indoor environment show that the proposed fusion approach achieves substantial positioning accuracy improvement than individual positioning approaches including PDR and WiFi positioning. View Full-Text

Keywords: indoor positioning; WiFi; inertial sensor; landmark; extended Kalman filter indoor positioning; WiFi; inertial sensor; landmark; extended Kalman filter





Autor: Zhi-An Deng 1,* , Guofeng Wang 1, Danyang Qin 2, Zhenyu Na 1, Yang Cui 3 and Juan Chen 1

Fuente: http://mdpi.com/



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