Data Fusion of Dual Foot-Mounted INS Based on Human Step Length Model

Pedestrian navigation methods based on inertial sensors are commonly used to solve navigation and positioning problems when satellite signals are unavailable. To address the issue of heading angle errors accumulating over time in pedestrian navigation systems that rely solely on the Zero Velocity Up...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 24; číslo 4; s. 1073
Hlavní autoři: Chen, Jianqiang, Liu, Gang, Guo, Meifeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 07.02.2024
MDPI
Témata:
Accelerometers
Accuracy
Algorithms
Analysis
Attitudes
Biomechanical Phenomena
bipedal constraint algorithm
Deep learning
Electronics in navigation
Foot
Gait
human step length model
Humans
Hypothesis testing
INS
Kinematics
MIMU
Motion
Motion capture
Navigation systems
pedestrian navigation
Pedestrians
Radio frequency identification
Sensors
ZUPT
MIMU
bipedal constraint algorithm
ZUPT
pedestrian navigation
human step length model
INS
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí:Pedestrian navigation methods based on inertial sensors are commonly used to solve navigation and positioning problems when satellite signals are unavailable. To address the issue of heading angle errors accumulating over time in pedestrian navigation systems that rely solely on the Zero Velocity Update (ZUPT) algorithm, it is feasible to use the pedestrian’s motion constraints to constrain the errors. Firstly, a human step length model is built using human kinematic data collected by the motion capture system. Secondly, we propose the bipedal constraint algorithm based on the established human step length model. Real field experiments demonstrate that, by introducing the bipedal constraint algorithm, the mean biped radial errors of the experiments are reduced by 68.16% and 50.61%, respectively. The experimental results show that the proposed algorithm effectively reduces the radial error of the navigation results and improves the accuracy of the navigation.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24041073