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A Survey of Indoor Inertial Positioning Systems for Pedestrians
Abstract
With the continual miniaturisation of sensors and processing nodes, Pedestrian Dead Reckoning (PDR) systems are becoming feasible options for indoor tracking. These use inertial and other sensors, often combined with domain-specific knowledge about walking, to track user movements. There is currently a wealth of relevant literature spread across different research communities. In this survey, a taxonomy of modern PDRs is developed and used to contextualise the contributions from different areas. Techniques for step detection, characterisation, inertial navigation and step-and-heading-based dead-reckoning are reviewed and compared. Techniques that incorporate building maps through particle filters are analysed, along with hybrid systems that use absolute position fixes to correct dead-reckoning output. In addition, consideration is given to the possibility of using smartphones as PDR sensing devices. The survey concludes that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours. It concludes by identifying a detailed list of challenges for PDR researchers.
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Cited by
Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons
He, S., & Chan, S.-H. G. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2464084 · Cited by 1,355
Location Fingerprinting with Bluetooth Low Energy Beacons
Faragher, R., & Harle, R. (2015) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2015.2430281 · Cited by 879
Self-citationIndoor Positioning Systems Based on Visible Light Communication: State of the Art
Luo, J., Fan, L., & Li, H. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2743228 · Cited by 435
Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion
Filippeschi, A., Schmitz, N., Miezal, M., Bleser, G., Ruffaldi, E., & Stricker, D. (2017) · Sensors · doi:10.3390/s17061257 · Cited by 361
Enhanced fingerprinting and trajectory prediction for IoT localization in smart buildings
Lin, K., Chen, M., Deng, J., Hassan, M. M., & Fortino, G. (2016) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2543242 · Cited by 246
Metadata
- Authors
- Harle, R.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2013
- Volume
- 15
- Issue
- 3
- Pages
- 1281-1293
- ISSN
- 1553-877X (ISSN-L)
- OpenAlex
- W2044831755
- Cited by
- 814
- References
- 0
- FWCI
- 39.34
- Type
- journal article
- Language
- en
- Open access
- closed
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