Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter

Abstract

The Foot-mounted Inertial Pedestrian-Positioning System (FIPPS) based on the Micro-Inertial Measurement Unit (MIMU) is a good choice for the forest fire fighters when the Global Navigation Satellite System is unavailable. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial navigation. However, the performance of ZUPT is highly affected by the low accuracy and high noise of the MIMU. The accuracy of conventional ZUPT for attitude alignment is reduced by the zero offset of acceleration and the drift of a gyroscope during the standing phase. An initial alignment algorithm based on Adaptive Gradient Descent Algorithm (AGDA) is proposed. In the stepping phase, the extended Kalman filter (EKF) is often used to correct attitude and position in track estimation. However, the measurement noise of the EKF is influenced by the high-frequency acceleration and angular velocity. Thus, the accuracy of the attitude and position will decrease. A double-constrained extended Kalman filtering (DEKF) is proposed. An adaptive parameter positively correlated with the acceleration and angular velocity is set, and the measurement noise in the DEKF is adaptively adjusted. The performance of the proposed method is verified by implementing the pedestrian test trajectory using MPU-9150 MIMU manufactured by InvenSense. The results show that the attitude error of the AGDA is 33.82% less than that of the conventional GDA. The attitude error of DEKF is 21.70% less than that of the conventional EKF. The experimental results verify the effectiveness and applicability of the proposed method.

Real-Time Height Measurement for Moving Pedestrians

“…Kalman filtering is a highly efficient recursive filter that can estimate the state of a dynamic system from a series of measurements containing redundant noise [40]. It can generate estimates of unknown variables, which have proven to be more accurate than those only based on a single measurement [4,41]. e Kalman filter can be implemented in two stages: time update stage and measurement update stage [42].…”

“…Whether in reality or in virtual scene, it is crucial to evaluate the height of moving pedestrians. Although there are many works related to dynamic pedestrians, such as detection and recognition [1][2][3], positioning [4][5][6], and tracking [7][8][9], it is still a serious challenge to measure the human height accurately in the dynamic case. As a vital state attribute of pedestrians, the height can not only help locate dynamic pedestrians or track criminal suspects in reality but also help people get rid of the 3D glasses or helmets in virtual scene [10].…”