Dynamic Compensation for Environmental Magnetic Interference in Pedestrian Dead Reckoning Using MEMS Sensors

When pedestrian positioning based on the global navigation satellite system is limited, inertial navigation based on the pedestrian dead reckoning (PDR) system using microelectromechanical system (MEMS) magnetic, angular rate, and gravity sensors is preferred. However, the magnetometer in MEMS is sensitive to the environmental magnetic interference, which significantly affects the positioning accuracy of PDR. This paper proposes a method for PDR based on zero-velocity-update (ZUPT) algorithm and extended Kalman filter (EKF) using MEMS sensors, which includes environmental magnetic interference detection and dynamic compensation. In the zero-velocity interval of the ZUPT algorithm, mean filter and threshold decision are used to discriminate interference and estimate the parameters of magnetic interference based on the recursive least square algorithm. Then the compensated magnetic field is used to calibrate the attitude and reduce the position error of the EKF-ZUPTbased PDR algorithm. Finally, the results of simulation and experiment indicate that the proposed method can effectively improve the anti-magnetic interference ability of PDR.