Abstract
Wireless localization methods are often subject to errors due to radio signal fluctuations that are used to estimate inter-device separation distances. We propose a novel method called MLRefine to counter these effects by refining RSS measurement data to obtain more accurate values that can enhance ranging and localization accuracies. MLRefine uses machine learning methods to model the relationship between accurate values and features extracted from in silico RSS values. MLRefine then applies the trained model to features extracted from real RSS measurement values to return a predicted set of refined RSS values. The refined RSS values are shown through computer simulations and real experiments to improve localization accuracy.
Original language | English |
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Title of host publication | 2018 IEEE Globecom Workshops, GC Wkshps 2018 |
Subtitle of host publication | Proceedings of a meeting held 9-13 December 2018, Abu Dhabi, United Arab Emirates. |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1506-1512 |
ISBN (Electronic) | 9781538649206 |
ISBN (Print) | 9781538649213 |
DOIs | |
Publication status | Published - 19 Feb 2019 |
Event | 2018 IEEE Globecom Workshops, GC Wkshps 2018 - Abu Dhabi, United Arab Emirates Duration: 9 Dec 2018 → 13 Dec 2018 |
Conference
Conference | 2018 IEEE Globecom Workshops, GC Wkshps 2018 |
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Country/Territory | United Arab Emirates |
City | Abu Dhabi |
Period | 9/12/18 → 13/12/18 |
Keywords
- IoT networks
- machine learning
- measurement refinement
- Wireless localization
- wireless sensor networks