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Pattern Synthesis of Linear Antenna Array Using Improved Differential Evolution Algorithm with SPS Framework.

Ruimeng Zhang1, Yan Zhang1, Jinping Sun1

  • 1School of Electronics & Information Engineering, Beihang University, Beijing 100191, China.

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|September 15, 2020
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Summary
This summary is machine-generated.

A novel SPS-JADE algorithm enhances linear antenna array pattern synthesis by optimizing excitation amplitudes. This method achieves superior sidelobe suppression and null depth compared to existing random optimization techniques.

Keywords:
SPS frameworkSPS-JADE algorithmantenna arraydifferential evolution algorithmnull depthpattern synthesisrandom optimization algorithmsidelobe suppression

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Area of Science:

  • Electromagnetics and Antenna Theory
  • Computational Intelligence
  • Optimization Algorithms

Background:

  • Linear antenna array pattern synthesis is crucial for directed signal transmission.
  • Traditional optimization methods face challenges with stagnation and achieving optimal performance.
  • The differential evolution (DE) algorithm offers a robust framework for complex optimization tasks.

Discussion:

  • The study introduces the successful-parent-selecting (SPS) framework to address DE algorithm stagnation.
  • The JADE algorithm, an improved DE variant, is integrated with SPS, creating the SPS-JADE algorithm.
  • Pattern synthesis is framed as an optimization problem targeting sidelobe suppression and null depth.

Key Insights:

  • The SPS-JADE algorithm demonstrates superior performance in linear antenna array pattern synthesis.
  • It achieves significantly lower sidelobe levels and improved null depth compared to state-of-the-art algorithms.
  • The algorithm effectively balances optimization objectives while adhering to beamwidth constraints.

Outlook:

  • The SPS-JADE algorithm presents a promising advancement for antenna array design.
  • Further research could explore its application in more complex antenna configurations and diverse optimization objectives.
  • This enhanced optimization approach has the potential to improve the efficiency and effectiveness of wireless communication systems.