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Radiation Pattern Shaping by Applying Machine Learning Method

Yang, Jiashu; (2022) Radiation Pattern Shaping by Applying Machine Learning Method. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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In this study, a new design method for designing surface wave antennas was proposed. The proposed antennas will support the vertical-looking radar (VLR) systems for better pest monitoring. The distribution of the metallic cells on the low-profile surface wave antenna is designed by using the Wasserstein generative adversarial network (WGAN) and bidirectional gated recurrent unit (Bi-GRU) neural network prediction method with the desired cosecant-squared radiation pattern serving as input. The proposed neural network prediction method consists of two parts, which are i) from the far-field radiation pattern to the near-zone E-field and ii) from the near-zone E-field to the on-surface metallic cell pattern. In the first prediction part, the average prediction error among Ex, Ey and Ez components on the surface wave antenna of 50 test cases is 4.3%. And the average prediction accuracy achieves 99.54% in the prediction of the metallic cell pattern from the near-zone E-field. A dual-sided 30º cosecant-squared radiation pattern serves as the input for the neural network prediction model in the surface wave antenna design. The predicted antenna geometry shows less than 1 dBi variation in radiation pattern when compared to the input dual-sided 30º cosecant-squared radiation pattern. The fabricated surface wave antenna works in the frequency band 33.77 – 35.05 GHz, which covers the frequency band of the mmWave FMCW VLR system. With the help of the turntable of the mmWave VLR system, such antenna provides a circular observation area with a diameter of 9.8 m.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Radiation Pattern Shaping by Applying Machine Learning Method
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2022. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Engineering Science Faculty Office
UCL > Provost and Vice Provost Offices > UCL BEAMS
URI: https://discovery.ucl.ac.uk/id/eprint/10147446
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