A single foot-mounted pedestrian navigation algorithm based on the maximum gait displacement constraint in three-dimensional space. (1st May 2022)

• Record Type: Journal Article
• Authors: Wang, Jianyu; Liu, Jinhao; Xu, Xiangbo; Yu, Zhibin; Li, Zhe
• Is Part Of: Measurement science & technology. Volume 33:Number 5(2022)
• Publication Date: 2022-05-01
• Subjects: IMU -- pedestrian positioning -- position error -- single foot constraint -- ZUPT; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac471b
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Inertial navigation technology composed of inertial sensors is widely used in foot-mounted pedestrian positioning. However, inertial sensors are susceptible to noise, which affects the performance of the system. Zero-velocity update (ZUPT) is a traditional method utilized to suppress the cumulative error. Unfortunately, the walking distance calculated by a Kalman filter still has positional error. To improve the positional accuracy, in this work we propose a nonlinear Kalman filter with a spatial distance inequality constraint for a single foot. Since the stride distance between adjacent stance phases has an upper bound in plane and height, an inertial navigation system established by one inertial measurement unit is adopted to constrain the stride process. Eventually, the performance of the proposed method is verified by experiments. Compared with the single foot-mounted ZUPT method, the proposed method suppresses the plane error and the height error by 46.04% and 65.48%, respectively. For the dual-foot constraint method, the proposed constraint method can reduce the number of sensors while ensuring positioning accuracy. Moreover, the height error is reduced by 59.98% on average by optimizing the constraint algorithm. The experimental results show that the trajectory estimated by the proposed method is closer to the actual path.