一种基于粒子群算法的多目标子阵划分优化方法

胡尚坤; 孙雨泽; 杨小鹏; 曾涛; 龙腾

doi:10.16798/j.issn.1003-0530.2017.08.014

一种基于粒子群算法的多目标子阵划分优化方法

北京理工大学嵌入式实时信息处理技术北京市重点实验室
北京理工大学信息与电子学院

基金项目: 高等学校学科创新引智计划资助项目（B14010）；国家自然科学基金资助项目（61671065，61225005，61427802）

详细信息

通讯作者:
杨小鹏 E-mail: xiaopengyang@bit.edu.cn

中图分类号: TN958
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出版历程
- 收稿日期: 2016-10-30
- 修回日期: 2017-03-16
- 发布日期: 2017-08-24

A Multi-Objective Optimization Subarray Partition Method Based On Particle Swarm Optimization

Beijing Key Laboratory of Embedded Real-time Information Processing Technology, Beijing Institute of Technology
School of Information and Electronics, Beijing Institute of Technology

More Information

Corresponding author:
YANG Xiao-peng E-mail: xiaopengyang@bit.edu.cn

摘要

摘要: 为了降低硬件成本和系统的复杂度，子阵划分对于大型的相控阵雷达来说是必要的。传统的子阵划分方法主要针对信号处理的单一性能指标优化。针对多项指标优化的问题，本文提出了一种基于粒子群算法的子阵划分结构优化算法，相对于传统的方法能够同时优化多项性能指标，提高信号处理的性能。通过对线性阵列的划分做仿真，展示了粒子群算法对子阵级波束形成多项性能指标的提高。
- 相控阵 /
- 子阵划分 /
- 智能算法 /
- 适应度函数 /
- 多目标优化
Abstract: In order to debase hardware cost and system complexity, subarray partition is necessary for large phased array antenna. Traditional subarray partition methods are mainly aimed at the single performance optimization of signal processing. In this paper, a subarray structure optimization algorithm based on particle swarm optimization (PSO) is proposed, which can optimize the multiple performances of the signal processing and improve it compared with the traditional methods. Through the simulation of the division of the linear array, the paper demonstrates that particle swarm optimization (PSO) algorithm is proposed to improve the performances of the subarray beamforming.
- phased array antenna /
- subarray partition /
- intelligence algorithm /
- fitness function /
- Multi-performances optimization

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参考文献(10)

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