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User Orientation Detection in Relation to Antenna Geometry in Ultra-Wideband Wireless Body Area Networks Using Deep Learning
Abstract
In this paper, the issue of detecting a user’s position in relation to the antenna geometry in ultra-wideband (UWB) off-body wireless body area network (WBAN) communication using deep learning methods is presented. To measure the impulse response of the channel, a measurement stand consisting of EVB1000 devices and DW1000 radio modules was developed and indoor static measurement scenarios were performed. It was proven that for the binary classification of user orientation, neural networks achieved accuracy that was more than 9% higher than that for the well-known threshold method. In addition, the classification of user position angles relative to the reference node was analyzed. It was proven that, using the proposed deep learning approach and the channel impulse response, it was possible to estimate the angle of the user’s position in relation to the antenna geometry. Absolute user orientation angle errors of about 4–7◦ for convolutional neural networks and of about 14–15◦ for multilayer perceptrons were achieved in approximately 85% of the cases in both tested scenarios.
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Authors (2)
-
Sebastian Urwan mgr inż.
- Intel Technology Poland Sp. z o.o.
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/s24072060
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Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach dostępnych w wersji elektronicznej [także online]
- Published in:
-
SENSORS
ISSN: 1424-8220 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Urwan S., Cwalina K., User Orientation Detection in Relation to Antenna Geometry in Ultra-Wideband Wireless Body Area Networks Using Deep Learning, SENSORS, 2024,10.3390/s24072060
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/s24072060
- Sources of funding:
-
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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