Free-walking: Pedestrian inertial navigation based on dual foot-mounted IMU

Wang, Qu; Fu, Meixia; Wang, Jianquan; Sun, Lei; Huang, Rong; Li, Xianda; Jiang, Zhuqing; Huang, Yan; Jiang, Changhui

doi:10.1016/j.dt.2023.03.001

Cited by 7 publications

(1 citation statement)

References 47 publications

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“…This method can improve the positioning accuracy of foot-mounted inertial navigation systems. Unlike the current widely used data-driven methods [23,24] that often require extensive datasets for training, our method demonstrates efficacy even with smaller datasets. This not only reduces the dependence on vast data resources, but also makes our approach more accessible to a broader range of researchers with varying computational capacities.…”

Section: Methodsmentioning

confidence: 99%

A Novel Zero-Velocity Interval Detection Algorithm for a Pedestrian Navigation System with Foot-Mounted Inertial Sensors

Wang,

Li,

et al. 2024

Sensors

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The zero-velocity update (ZUPT) algorithm is a pivotal advancement in pedestrian navigation accuracy, utilizing foot-mounted inertial sensors. Its key issue hinges on accurately identifying periods of zero-velocity during human movement. This paper introduces an innovative adaptive sliding window technique, leveraging the Fourier Transform to precisely isolate the pedestrian’s gait frequency from spectral data. Building on this, the algorithm adaptively adjusts the zero-velocity detection threshold in accordance with the identified gait frequency. This adaptation significantly refines the accuracy in detecting zero-velocity intervals. Experimental evaluations reveal that this method outperforms traditional fixed-threshold approaches by enhancing precision and minimizing false positives. Experiments on single-step estimation show the adaptability of the algorithm to motion states such as slow, fast, and running. Additionally, the paper demonstrates pedestrian trajectory localization experiments under a variety of walking conditions. These tests confirm that the proposed method substantially improves the performance of the ZUPT algorithm, highlighting its potential for pedestrian navigation systems.

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Section: Methodsmentioning

confidence: 99%