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基于混合三角变异差分进化算法的平面稀疏阵列约束优化

陈志坤 杜康 彭冬亮 朱新挺

陈志坤, 杜康, 彭冬亮, 朱新挺. 基于混合三角变异差分进化算法的平面稀疏阵列约束优化[J]. 电子与信息学报, 2020, 42(4): 895-901. doi: 10.11999/JEIT190705
引用本文: 陈志坤, 杜康, 彭冬亮, 朱新挺. 基于混合三角变异差分进化算法的平面稀疏阵列约束优化[J]. 电子与信息学报, 2020, 42(4): 895-901. doi: 10.11999/JEIT190705
Zhikun CHEN, Kang DU, Dongliang PENG, Xinting ZHU. Planar Sparse Array Constraint Optimization Based on Hybrid Trigonometric Mutation Differential Evolution Algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(4): 895-901. doi: 10.11999/JEIT190705
Citation: Zhikun CHEN, Kang DU, Dongliang PENG, Xinting ZHU. Planar Sparse Array Constraint Optimization Based on Hybrid Trigonometric Mutation Differential Evolution Algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(4): 895-901. doi: 10.11999/JEIT190705

基于混合三角变异差分进化算法的平面稀疏阵列约束优化

doi: 10.11999/JEIT190705
基金项目: 国家自然科学基金(61701148)
详细信息
    作者简介:

    陈志坤:男,1982年生,博士,讲师,研究方向为雷达阵列信号处理与电子侦察

    杜康:男,1996年生,硕士生,研究方向为阵列优化与波束形成

    彭冬亮:男,1977年生,博士,教授,博士生导师,研究方向为信息融合

    朱新挺:男,1996年生,硕士生,研究方向为信号检测技术

    通讯作者:

    杜康 dk@hdu.edu.cn

  • 中图分类号: TN957.2

Planar Sparse Array Constraint Optimization Based on Hybrid Trigonometric Mutation Differential Evolution Algorithm

Funds: The National Natural Science Foundation of China (61701148)
  • 摘要:

    针对旁瓣零陷凹面约束的稀疏平面阵列优化及算法早熟等问题,该文基于参数自适应的思想,提出一种混合三角变异差分进化算法。通过引入旁瓣零陷凹面约束矩阵,构建自适应惩罚函数,时变权重组合变异策略与交叉策略,提高算法前期全局搜索能力和后期收敛能力,最终实现峰值旁瓣电平和旁瓣零陷凹面的平面阵列约束优化。仿真结果表明,对比混合三角变异策略前的算法,该算法在完成稀疏阵列峰值旁瓣电平优化的同时,能在指定旁瓣区域完成零陷凹面设计,降低有源干扰影响。

  • 图  1  稀疏阵列3维方向图

    图  2  算法收敛曲线对比和稀疏阵元分布

    图  3  可行解比例和$u = 0$比平面方向图

    图  4  稀疏阵列3维方向图

    图  5  算法收敛曲线和稀疏阵元分布

    图  6  可行解比例和$u = 0$平面方向图

    表  1  最大零陷深度约束为45时旁瓣零陷凹面增益(c = 1)

    序号123456789
    $p$505050515151525252
    $q$505152505152505152
    增益(dB)–41.7232–47.8437–43.4869–41.2586–53.0450–44.7019–43.8560–46.1852–46.9309
    下载: 导出CSV

    表  2  最大零陷深度约束为50时旁瓣零陷凹面增益(c = 1)

    序号123456789
    $p$505050515151525252
    $q$505152505152505152
    增益(dB)–46.6703–45.7740–42.0270–43.5748–55.1658–43.9545–42.9269–49.6869–45.4186
    下载: 导出CSV

    表  3  最大零陷深度约束为55时旁瓣零陷凹面增益(c = 1)

    序号123456789
    $p$505050515151525252
    $q$505152505152505152
    增益(dB)–46.4656–47.1974–43.3241–47.4544–58.0909–43.7558–55.5215–48.7782–45.2064
    下载: 导出CSV

    表  4  最大零陷深度约束为45时旁瓣零陷凹面增益(c = 2)

    序号123456789
    p494949494950505050
    q495051525349505152
    增益(dB)–46.5458–45.8412–46.2580–40.8917–40.9214–49.3585–55.9305–47.3353–42.1900
    序号101112131415161718
    p505151515151525252
    q534950515253495051
    增益(dB)–42.6126–43.5500–51.2554–49.3633–44.1652–43.3289–44.5767–60–60
    序号19202122232425
    p52525353535353
    q52534950515253
    增益(dB)–45.5003–42.0649–46.5876–45.0475–48.2879–44.7265–41.0207
    下载: 导出CSV

    表  5  最大零陷深度约束为50时旁瓣零陷凹面增益(c = 2)

    序号123456789
    p494949494950505050
    q495051525349505152
    增益(dB)–37.6175–40.3846–45.7498–48.2500–43.6255–36.8799–41.7660–49.6858–45.8806
    序号101112131415161718
    p505151515151525252
    q534950515253495051
    增益(dB)–41.8181–37.2718–43.5080–60–45.8777–39.6356–39.5716–45.9265–60
    序号19202122232425
    $p$52525353535353
    $q$52534950515253
    增益(dB)–47.8587–39.4033–43.4406–50.7166–60–51.4716–40.3799
    下载: 导出CSV

    表  6  最大零陷深度约束为55时旁瓣零陷凹面增益(c = 2)

    序号123456789
    p494949494950505050
    q495051525349505152
    增益(dB)–44.8401–46.0399–39.5838–38.3594–45.4208–54.4196–59.5659–43.2692–43.0806
    序号101112131415161718
    p505151515151525252
    q534950515253495051
    增益(dB)–51.9257–41.3720–45.5307–55.5682–49.6248–40.8617–36.9498–40.1514–48.4430
    序号19202122232425
    $p$52525353535353
    $q$52534950515253
    增益(dB)–43.0303–37.3386–35.2466–38.9316–46.3749–42.4130–37.3552
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-09-10
  • 修回日期:  2019-11-28
  • 网络出版日期:  2020-01-11
  • 刊出日期:  2020-06-04

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