The Improved 3D Pedestrian Positioning System Based on Foot-Mounted Inertial Sensor

Abstract

The foot-mounted navigation system based on Micro Inertial Measurement Unit (MIMU) can estimate the position of pedestrians. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial sensor. However, the low accuracy and high noise of the MIMU will greatly affect the measurement accuracy of zero velocity detector, which will lead to poor 3D pedestrian positioning accuracy. An improved generalized likelihood ratio test (GLRT) method is proposed by establishing adaptive thresholds at different gait frequency. The core is to dynamically adjust the detection threshold according to different motion states, so as to determine the constraint relation for detection. In addition, the drift of sensors and the accumulated error of position integration affect the accuracy of attitude and position. Therefore, an improved extended Kalman particle filter (EKPF) is proposed. The improved EKPF constructs the position constraint model in altitude direction by introducing barometer information. The new particle filter distribution function is constructed by constrained multi-source observation information, which makes the posterior probability distribution closer to the real distribution. In order to verify the feasibility and effectiveness of this method, the inertial pedestrian navigation system is established. The experimental results demonstrate that the improved GLRT method performs well in detecting the zero velocity point of the foot, and improved EKPF method reduces the positioning error by 69.25% and 53.57% compared with the EKF and PF, respectively. Therefore, the proposed method can improved the 3D pedestrian positioning accuracy.