In this paper, we propose an advanced pedestrian dead-reckoning (PDR) algorithm that considers the heel-strike and toe-off phases. Generally, PDR systems that use a foot-mounted inertial measurement unit are based on an inertial navigation system with an extended Kalman filter (EKF). To reduce the influence of the bias and white noises in the gyroscope and accelerometer signals, a zero-velocity update is often adopted at the stance phase. However, transient and large acceleration, which cannot be measured by the accelerometer used in pedestrian navigation, occur momentarily in the heel-strike phase. The velocity information from integration of the acceleration is not reliable because the acceleration is not measured in the heel-strike phase. Therefore, the designed EKF does not correctly reflect the actual environment, because conventional algorithms do not take the non-measurable acceleration into consideration. In order to reflect the actual environment, we propose a PDR system that considers the non-measurable acceleration from the heel-strike impact. To improve the PDR system’s performance, the proposed algorithm uses a new velocity measurement obtained using the constraint between the surface and the foot during the toe-off phase. The experimental results show improved filter performance after comparison of the proposed algorithm and a conventional algorithm.

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In this paper, we analyze the position errors of the pedestrian dead reckoning (PDR) system using foot-mounted IMU attached to each foot, and implement PDR system using dual foot-mounted IMU to reduce the analyzed error. The PDR system using foot-mounted IMU is generally based on an inertial navigation system (INS). To reduce bias and white noise errors, INS is combined with zero velocity update (ZUPT), which assumes that the pedestrian shoe velocity is zero at the stance phase. Although ZUPT could compensate the velocity and position, the heading drift still occurs. When analyzing the characteristics of the position error, the error shows a symmetrical characteristic. In order to reduce this error, the previous researches compensate for both positions by applying feet position constraints. The algorithm consists of applying a conventional PDR system to each foot and fusion algorithm combining both. The PDR system using foot-mounted IMU, one on each foot, is based on integration approach separately. The positions of both feet should be in a circle with a radius as step length during walking. The designed filter is constrained so that the position of both feet are in a circular boundary. The heading error that is symmetrically drifted is corrected by the position constraint when the pedestrian moves straight. Experimental results show the performance and usability of each previous algorithm to compensate for symmetric heading errors.

In this study, the effect of acceleration matching according to sensor specifications in rapid transfer alignment is analysed. In general, the velocity and attitude information of the Master Inertial Navigation System (MINS) is used for transfer alignment. MINS angular velocity information is used to improve the alignment speed in shipboard transfer alignment. Acceleration matching, on the other hand, is generally considered an impractical option for transfer alignment. However, in the case of shipboard transfer alignment, acceleration matching is thought to be effective. In order to analyse the performance of acceleration matching, a performance index is defined and the efficiency of acceleration matching is analysed according to various sensor specifications and simulation environments. Based on the analysis of the estimated performance according to the simulation results, it is confirmed that acceleration matching in rapid transfer alignment is valid.