<p>This paper presents two high-performance series-fed antenna array configurations optimized for 24 GHz automotive radar in Internet of Things applications. The <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(2\times 5\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(4\times 5\)</EquationSource> </InlineEquation> array designs employ circular microstrip patches, excited via a custom-designed power divider, to ensure uniform amplitude and phase distribution. A key contribution is the integration of the Parallel Surrogate-Assisted Differential Evolution Algorithm (PSADEA) to optimize antenna parameters, resulting in improved reflection coefficient, realized gain, and beamwidth. The proposed arrays achieve measured gains of 16 dBi and 19.5 dBi, with radiation efficiencies of 96.55% and 95.55%, respectively. Both arrays exhibit low return loss across the 23–25 GHz band and maintain a compact size suitable for integration into vehicle platforms. A comparative analysis with state-of-the-art designs confirms the effectiveness of PSADEA-based optimization in improving design performance with fewer electromagnetic simulations. Finally, the results demonstrate that the proposed arrays are strong candidates for automotive radar in IoT applications.</p>

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Array antenna with series-fed configuration providing high radiation performances for automotive radar in IoT applications

  • Hassan Zakeri,
  • Mahdi Parvaneh,
  • Gholamreza Moradi,
  • Mohammad Alibakhshikenari,
  • Bal S. Virdee,
  • Mariana Dalarsson

摘要

This paper presents two high-performance series-fed antenna array configurations optimized for 24 GHz automotive radar in Internet of Things applications. The \(2\times 5\) and \(4\times 5\) array designs employ circular microstrip patches, excited via a custom-designed power divider, to ensure uniform amplitude and phase distribution. A key contribution is the integration of the Parallel Surrogate-Assisted Differential Evolution Algorithm (PSADEA) to optimize antenna parameters, resulting in improved reflection coefficient, realized gain, and beamwidth. The proposed arrays achieve measured gains of 16 dBi and 19.5 dBi, with radiation efficiencies of 96.55% and 95.55%, respectively. Both arrays exhibit low return loss across the 23–25 GHz band and maintain a compact size suitable for integration into vehicle platforms. A comparative analysis with state-of-the-art designs confirms the effectiveness of PSADEA-based optimization in improving design performance with fewer electromagnetic simulations. Finally, the results demonstrate that the proposed arrays are strong candidates for automotive radar in IoT applications.