In this paper, a sensor fusion algorithm that integrates gyroscope and vision measurements using an adaptive complementary Kalman filter is proposed to estimate the attitude of a hybrid head tracker system. In order to make the filter more tolerant to vision measurement fault and more robust to system dynamics, an adaptive fading filter is implemented to the sensor fusion filter, and fuzzy logic is applied to adjust the fading factor, which adapts a Kalman gain of the sensor fusion filter. For recognizing the dynamic condition of the system and vision measurement fault, the normalized square error of attitude and the norm of gyroscope output with designed membership functions are used. The performance of the proposed algorithm is evaluated by simulations. It is confirmed that the proposed algorithm has better performance than the conventional algorithms in high-dynamic conditions and vision measurement fault case.

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.

An optimal configuration for a redundant inertial measurement unit (RIMU) minimizing the lever arm effect and the constraints for the optimality are proposed. The proposed configuration provides the allocation of each sensor in an RIMU as well as the orientation, while conventional configurations, which do not consider the lever arm effect, suggest the orientation only. The orientation is fixed, so that the configuration may guarantee the best navigation performance, and the allocation of each sensor is determined for minimizing the lever arm effect and for the densest disposition of the structures. To qualify the navigation performance of a configuration, a novel figure of merit (FOM) indicating the worst navigation performance of an RIMU is proposed first. In general, an FOM, which indicates the averaged error covariance of all the estimation, is used as a navigation performance index. The proposed FOM, however, represents the worst error covariance, which contributes to the determination of a secure configuration against excessive noises. The optimal condition of the proposed FOM is also derived, in which case the proposed configuration guarantees the best navigation performance. Using the proposed FOM, the constraint for obtaining the optimal configuration, which guarantees the best navigation performance and minimizes the lever arm effect, is proposed. The effectiveness of the proposed method is confirmed via case studies and Monte Carlo simulations, which show that the RIMU with the proposed configuration sustains sufficiently high position accuracy in spite of the lever arm effect.

The design, fabrication, and evaluation results of a MEMS piezoresistive differential pressure sensor fabricated by the dry etching process are described in this paper. The proposed sensor is designed to have optimal performances in mid-pressure range from 0 psi to 20 psi suitable for a precision air data module. The piezoresistors with a Wheatstone bridge structure are implanted where the thermal effects are minimized subject to sustainment of the sensitivity. The rectangular-shaped silicon diaphragm is adopted and its dimension is analyzed for improving pressure sensitivity and linearity. The bridge resistors are driven by constant current to compensate temperature effects on sensitivity. The designed differential pressure sensor is fabricated by using MEMS dry etching techniques, and the fabricated sensing element is attached and packaged in a Kovar package in consideration of leakage and temperature hysteresis. The implemented sensors are tested and evaluated as well. The evaluation results show the static RSS (root sum square) accuracy including nonlinearity, non-repeatability, and pressure hysteresis before temperature compensation is about 0.09%, and the total error band which includes the RSS accuracy, the thermal hysteresis, and other thermal effects is about 0.11%, which confirm the validity of the proposed design process.

In this paper, we present a method for finding the enhanced heading and position of pedestrians by fusing the Zero velocity UPdaTe (ZUPT)-based pedestrian dead reckoning (PDR) and the kinematic constraints of the lower human body. ZUPT is a well known algorithm for PDR, and provides a sufficiently accurate position solution for short term periods, but it cannot guarantee a stable and reliable heading because it suffers from magnetic disturbance in determining heading angles, which degrades the overall position accuracy as time passes. The basic idea of the proposed algorithm is integrating the left and right foot positions obtained by ZUPTs with the heading and position information from an IMU mounted on the waist. To integrate this information, a kinematic model of the lower human body, which is calculated by using orientation sensors mounted on both thighs and calves, is adopted. We note that the position of the left and right feet cannot be apart because of the kinematic constraints of the body, so the kinematic model generates new measurements for the waist position. The Extended Kalman Filter (EKF) on the waist data that estimates and corrects error states uses these measurements and magnetic heading measurements, which enhances the heading accuracy. The updated position information is fed into the foot mounted sensors, and reupdate processes are performed to correct the position error of each foot. The proposed update-reupdate technique consequently ensures improved observability of error states and position accuracy. Moreover, the proposed method provides all the information about the lower human body, so that it can be applied more effectively to motion tracking. The effectiveness of the proposed algorithm is verified via experimental results, which show that a 1.25% Return Position Error (RPE) with respect to walking distance is achieved.