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Improved RSS-Based DoA Estimation Accuracy in Low-Profile ESPAR Antenna Using SVM Approach

Abstrakt

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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Copyright (2019 IEEE)

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Kategoria:
Aktywność konferencyjna
Typ:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Język:
angielski
Rok wydania:
2019
Opis bibliograficzny:
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:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/etfa.2019.8868967
Bibliografia: test
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  2. A. Alsadi and S. Mohan, "Improving the Physical Layer Security of the Internet of Things (IoT)," 2018 IEEE International Smart Cities Conference (ISC2), Kansas City, MO, USA, 2018, pp. 1-8. otwiera się w nowej karcie
  3. F. Viani, L. Lizzi, M. Donelli, D. Pregnolato, G. Oliveri, and A. Massa, "Exploitation of parasitic smart antennas in wireless sensor networks," Journal of Electromagnetic Waves and Applications, vol. 24, no. 7, pp. 993-1003, Jan. 2010. otwiera się w nowej karcie
  4. M. Tarkowski, M. Rzymowski, L. Kulas and K. Nyka, "Improved Jamming Resistance Using Electronically Steerable Parasitic Antenna Radiator," 17th International Conference on Smart Technologies (EUROCON 2017), pp. 496-500, Jul. 2017. otwiera się w nowej karcie
  5. L. Kulas, "RSS-based DoA Estimation Using ESPAR Antennas and Interpolated Radiation Patterns," IEEE Antennas Wireless Propag. Lett., vol. 17, pp.25-28, 2018. otwiera się w nowej karcie
  6. S. Chandran, Advances in Direction-of-Arrival Estimation. London, U.K.: Artech House, 2005.
  7. Rzymowski, P. Woznica, and L. Kulas, "Single-Anchor Indoor Localization Using ESPAR Antenna," IEEE Antennas Wireless Propag. Lett., vol. 15, pp. 1183-1186, 2016. otwiera się w nowej karcie
  8. M. Burtowy, M. Rzymowski, and L. Kulas, "Low-Profile ESPAR Antenna for RSS-Based DoA Estimation in IoT Applications," IEEE Access, vol. 7, pp. 17403-17411, 2019. otwiera się w nowej karcie
  9. M. Tarkowski and L. Kulas, " RSS-based DoA Estimation for ESPAR Antennas Using Support Vector Classification," IEEE Antennas Wireless Propag. Lett., vol. 18, no. 4, pp. 561-565, Apr. 2019. otwiera się w nowej karcie
  10. M. Tarkowski, M. Rzymowski, K. Nyka and L. Kulas, "RSS-Based DoA Estimation with ESPAR Antennas Using Reduced Number of Radiation Patterns," in Proc. 13th Eur. Conf. Antennas Propag. (EuCAP 2019), Cracow, PL, 2019, in press. otwiera się w nowej karcie
  11. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," in IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, Aug. 2004. otwiera się w nowej karcie
  12. Aurelien Geron, Hands-On Machine Learning with Scikit-Learn & TensorFlow. Sebastopol, CA: O'Reilly Media, Inc., 2017, pp. 156-165 otwiera się w nowej karcie
  13. M. Plotka, M. Tarkowski, K. Nyka, and L. Kulas, "A Novel Calibration Method for RSS-Based DoA Estimation Using ESPAR Antennas," 22nd International Conference on Microwaves, Radar and Wireless Communications (MIKON 2018), Poznan, PL, 2018, pp. 65-68. otwiera się w nowej karcie
  14. C. Chang and C. Lin, LIBSVM: a library for support vector machines, 2001. otwiera się w nowej karcie
  15. This work was supported by SCOTT (www.scott-project.eu) project that has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 737422. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and Austria, Spain, Finland, Ireland, Sweden, Germany, Poland, Portugal, Netherlands, Belgium, Norway. otwiera się w nowej karcie
Źródła finansowania:
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Weryfikacja:
Politechnika Gdańska

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