[time]
2020-11-18T08:32:42+01:00

[team_name]
WHUGNSS

[team_institution]
Wuhan university

[logolink]


[system_name]


[website]


[track]
4

[reference_person]
Jian Kuang; Tao Liu; Yan Wang

[email]
kuang@whu.edu.cn

[description]
Short: The classic zero-velocity update algorithm (ZUPT) based foot-mounted pedestrian dead reckoning consists of a strap-down inertial navigation algorithm, a stance phase detection algorithm, and an error state Kalman filter. However, the classic ZUPT-based Foot-PDR cannot be overcome the influence of the complex motion of the pedestrian. Several schemes are designed to improve navigation performance. 1) An improved adaptive threshold algorithm to detect the stance-phase in each gait cycle. 2) The zero angular rate update (ZARU) algorithm, the improved heuristic drift elimination (iHDE), and the straight-line constraint algorithm are used to constraint the heading error drift. 3) A motion detection algorithm is used to distinguish ground, escalator, and elevator, and a constant speed constraint is used to update the velocity vector when the pedestrian takes the escalator and elevator. 4) The calibrated magnetometer observations are used to detect whether the user returns to the same area. 5) The loosely integrated model is used to combine Foot-PDR and GNSS signals, and an adaptively robust algorithm is used to improve the performance of the Kalman filter. 6) The optimal inertial sensor parameters (i.e., the bias instability of gyroscopes and accelerometers, the angular random walk, and the velocity random walk) are determined through the provided long-term static data.

Long: The classic zero-velocity update algorithm (ZUPT) based foot-mounted pedestrian dead reckoning consists of a strap-down inertial navigation algorithm, a stance phase detection algorithm, and an error state Kalman filter. However, the classic ZUPT-based Foot-PDR cannot overcome the influence of the complex motion of the pedestrian. Several schemes are designed to improve navigation performance.
1)The classic generalized likelihood ratio test (GLRT) method is one of the most common algorithms for detecting the stance phase. Here, we use an improved adaptive threshold instead of the fixed threshold method to detect the stance-phase in each gait cycle. The adaptive threshold method is adaptable to different gait frequencies in dynamic motion. Once the stance phase is detected, a zero velocity vector is used to estimate and correct the navigation error. 
2)The heading angle error and the z-axis gyroscope bias of the ZUPT algorithm are unobservable. Thus, we use the following methods to constrain the error divergence of the heading angle. Firstly, the zero angular rate update (ZARU) algorithm is employed to estimate the gyroscope bias and heading angle error. Compared with the stance phase detection algorithm, we use a stricter fixed threshold and a more extended continuous period to determine the update chance of ZARU. Furthermore, when a pedestrian is determined to be walking in a straight-line path or the corridor's primary orientation, the improved heuristic drift elimination (iHDE) and the straight-line constraint algorithms are applied to estimate the heading angle and the z-axis gyroscope bias. These algorithms can effectively improve the performance and reliability of pedestrian navigation.
3)The height error divergence is also a significant problem in Foot-PDR, especially for multi-floor navigation and positioning application. In the absence of a barometer, we adopted an effective height constraint algorithm to reduce the error drift along the vertical channel. When pedestrians go up and downstairs, the slope angle can be considered constant in most cases. In our solution, the stride length and the slope angle between adjacent footsteps are used to determine whether the pedestrian is walking on a plane or going up and downstairs. Then the slope-based or plane-based height constraint algorithm is used to improve the estimated height accuracy in Foot-PDR.
The other extreme scenario is the escalator or lift. Usually, escalators run at a constant speed. When a pedestrian stands relatively static on the escalator, the specific forces measured by the foot-mounted IMU are almost all derived from local gravity. The gravity information can be fused in a tightly coupled manner in our solution, so the drifting error can be constrained even when a pedestrian stands still on an escalator. Moreover, when the pedestrian takes a lift, the specific forces (i.e., the accelerations) will exhibit clear acceleration motion and deceleration motion process. The vertical (up or down) velocity information of the pedestrian can be estimated using acceleration and deceleration motions. Thus, the vertical velocity can be as observation information to improve the performance and stability of the Foot-PDR.
4)Many ferromagnetic materials exist in indoor building structures. So, magnetometers cannot be used to determine the heading angle in Foot-PDR directly. Yet, combined with a rough position, the magnetic field signals can recognize similar areas when the pedestrian returns to places they have walked before. This meaningful information can help improve the robustness of Foot-PDR in practical application.
5)The Foot-PDR is integrated with GNSS signals in a loosely-coupled manner. Satellites with small elevations should be discarded to avoid the gross error as much as possible. Besides, some measurements with low quality judged by the innovation vector's magnitude and covariance need to be rejected in the Kalman filter. Furthermore, the adaptively robust filtering algorithm is used to control the effects of inaccurate measurements in our solution and improve system accuracy.
6)The optimal inertial sensor parameters (i.e., the bias instability of gyroscopes and accelerometers, the angular random walk, and the velocity random walk) are determined through the provided long-term static data. The magnetometer is also calibrated through the classic ellipsoid fitting method.
Through the above processing, our algorithm can give continuous and effective positioning results of the scene similar to track 4.


[references]