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Abstract
In this paper, we have shown how the overall performance of direction-of-arrival (DoA) estimation using lowprofile electronically steerable parasitic array radiator (ESPAR) antenna, which has been proposed for Internet of Things (IoT) applications, can significantly be improved when support vector machine (SVM) approach is applied. Because the SVM-based DoA estimation method used herein relies solely on received signal strength (RSS) values recorded at the antenna output port for different directional radiation patterns produced by the antenna steering circuit, the algorithm is wellsuited for IoT nodes based on inexpensive radio transceivers. Measurement results indicate that, although the antenna can provide 8 unique main beam directions, SVM-based DoA of unknown incoming signals can successfully be estimated with good accuracy in a fast way using limited number of radiation patterns. Consequently, such an approach can be used in efficient location-based security methods in Industrial Internet of Things (IIoT) applications.
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- Publication version
- Accepted or Published Version
- License
- Copyright (2019 IEEE)
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2019
- Bibliographic description:
- Tarkowski M., Burtowy M., Rzymowski M., Nyka K., Groth M., Kulas Ł.: Improved RSS-Based DoA Estimation Accuracy in Low-Profile ESPAR Antenna Using SVM Approach// / : , 2019,
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/etfa.2019.8868967
- Sources of funding:
- Verified by:
- Gdańsk University of Technology
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