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.
Highlights
High-precision pedestrian navigation systems usually include GPS and autonomous navigation. ese systems can generate real-time data for a pedestrian’s attitude and position that can be widely used in re protection, patrol, and military elds [1, 2]
To improve the positioning accuracy, the attitude angle corrected by the Adaptive Gradient Descent Algorithm (AGDA) and the attitude angle corrected by the quaternion method are fused by the improved extended Kalman filter (EKF) and the noise interference of acceleration and angular velocity is constrained adaptively by dualconstrained extended Kalman filter (DEKF). e state equation and observation equation of the navigation system model are shown in the following equations: X−k FkX−k− 1 + Wk, (18)
In the DEKF, τ is used to estimate the high-frequency interference caused by the change of acceleration and angular velocity in the stepping phase. e measurement noise of the DEKF is constrained by adaptive adjustment of parameter τ. erefore, the attitude and position errors of the pedestrian positioning system are reduced
Summary
High-precision pedestrian navigation systems usually include GPS and autonomous navigation. ese systems can generate real-time data for a pedestrian’s attitude and position that can be widely used in re protection, patrol, and military elds [1, 2]. When the step size is small, it is easy to fall into local optimal solutions, that is, to obtain a minimum point that is not minimum value To overcome this problem, a gradient descent algorithm based on the adaptive step size is proposed that combines the acceleration and angular velocity measurements in the step size. Erefore, the noise of the accelerometer and gyroscope in the dualconstrained extended Kalman filter (DEKF) method proposed in this paper is considered in the measurement noise, that is, adding adaptive parameters can be used to suppress the high-frequency noise in the measurement noise, which helps prevent filter divergence and ensures the accuracy of attitude and position.
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