Abstract
To improve the accuracy of indoor pedestrian positioning, an indoor pedestrian positioning system with two-order Bayesian estimation based on Extended Kalman Filter (EKF) and Particle Filter (PF) is proposed in this paper. The presented system combines a foot-mounted inertial sensor, a Wi-Fi propagation model and building structure to make good use of these information resources. There are two updates in this system in order to limit the accumulative errors of inertial sensors. In the first update, the inertial navigation system (INS) is the main system in the calculation of pedestrian positioning, and Zero-velocity update (ZUPT) is introduced as the reference to correct the accumulative errors of INS based on EKF. To further limit the accumulative errors of inertial sensors, the estimated results obtained from the first update, including horizontal position information, are introduced as the observations based on PF in the second update; Pedestrian Dead Reckoning (PDR) is the main system in the calculation of pedestrian positioning, and the weight of particles is determined by the Wi-Fi propagation model, building structure information and output of the first update. The results show that the accuracy of positioning is effectively increased.
Highlights
Global Positioning System (GPS) is a widely used positioning system which can provide highly accurate outdoor position information in real-time and in all weather conditions
inertial navigation system (INS) plays a major role in the calculation of pedestrian positioning, and Zero-velocity update (ZUPT) is introduced as the reference to correct the accumulative errors of INS based on Extended Kalman Filter (EKF); in the second update, the estimated results obtained from the first update, which are horizontal position information, are introduced as the observations in the second update
Pedestrian Dead Reckoning (PDR) plays a major role in the calculation of pedestrian positioning; the method of data fusion is Particle Filter (PF), the particle weight is determined by the Wi-Fi propagation model, building structure information and output of first-order data fusion
Summary
Global Positioning System (GPS) is a widely used positioning system which can provide highly accurate outdoor position information in real-time and in all weather conditions. The frame combines INS with ZUPT based on EKF to limit accumulative errors, and a single foot-mounted inertial sensor is used to collect pedestrian state information. The work in [16] obtains the position information of the pedestrian by combining Wi-Fi fingerprinting and inertial sensors based on PF; the three methods introduce Wi-Fi fingerprinting as additional information, which requires a pre-survey for the offline fingerprinting map. INS plays a major role in the calculation of pedestrian positioning, and ZUPT is introduced as the reference to correct the accumulative errors of INS based on EKF; in the second update, the estimated results obtained from the first update, which are horizontal position information, are introduced as the observations in the second update.
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