Abstract
The task of indoor positioning is fundamental to several applications, including navigation, healthcare, location-based services, and security. An emerging field is inertial navigation for pedestrians, which relies only on inertial sensors for positioning. In this paper, we present inertial pedestrian navigation models and learning approaches. Among these, are methods and algorithms for shoe-mounted inertial sensors and pedestrian dead reckoning (PDR) with unconstrained inertial sensors. We also address three categories of data-driven PDR strategies: activity-assisted, hybrid approaches, and learning-based frameworks.
Original language | English |
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Article number | 104077 |
Journal | Results in Engineering |
Volume | 25 |
DOIs | |
State | Published - Mar 2025 |
Bibliographical note
Publisher Copyright:© 2025 The Author
Keywords
- Inertial sensors
- Machine learning
- Pedestrian dead reckoning
ASJC Scopus subject areas
- General Engineering