Open AccessLearning of Zero-Velocity Detection for Inertial Pedestrian Navigation
Author(s) -
Ze Chen,
Xianfei Pan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1846/1/012055
Subject(s) - zero (linguistics) , inertial navigation system , detector , metric (unit) , artificial intelligence , computer science , calibration , zero crossing , control theory (sociology) , physics , inertial frame of reference , mathematics , engineering , optics , statistics , control (management) , classical mechanics , philosophy , linguistics , operations management , quantum mechanics , voltage
The detection of zero-velocity states is the vital prerequisite for zero-velocity update in the foot-mounted inertial pedestrian navigation system. The previous zero-velocity detector determines zero-velocity states by comparing measured inertial data with a calibrated threshold. The calibration of the threshold is inconvenient for this kind of the zero-velocity detector because the threshold is variable corresponding to different people and locomotion. The best threshold needs to be tuned corresponding to different situations. In essence, the detection of zero-velocity states is a binary classification problem. As the success of deep learning in in image classification and speech recognition, it is possible to design an adaptive zero-velocity detector based on it. A Siamese network is designed to learn the metric of distinguish zero-velocity states. This method can adaptively get the most likely correct results without threshold tuning. Experiments are conducted and results show that the matching degree is about 96.31% and the navigation accuracy can reach within 4m in 20min.