首页|期刊导航|雷达科学与技术|SEEFO算法驱动的多约束稀布阵列优化方法研究

SEEFO算法驱动的多约束稀布阵列优化方法研究OA

Sparse Array Optimization Under Multiple Constraints Driven by the SEEFO

中文摘要英文摘要

针对具有最小间距约束等多约束条件下的阵列天线优化问题,本文提出一种结合Sobol序列的电鳗觅食优化(EEFO)算法和引入高斯扰动的天线方向图优化方法.首先,利用密度加权法对阵列预处理,提高阵列优化效率;其次,在密度加权阵列的基础上,引入Sobol序列对种群进行初始化,接着采用EEFO算法进一步优化阵列天线各单元的位置,以搜索全局最优解;最后,为了突破非对称矩阵映射方法中把求解实际距离转化为求解两个映射矩阵的局限性,对优化后的阵列加入高斯扰动,充分提高阵列自由度.实验结果表明,本文方法可降低优化算法计算成本,提升阵元自由度,有效降低阵列峰值旁瓣电平.

To address the array antenna optimization problem under multiple constraints,including a minimum el-ement spacing constraint,a radiation pattern optimization method that integrates a Sobol sequence-based electric eel for-aging optimization(EEFO)algorithm with Gaussian perturbation is proposed in this paper.Firstly,the density-weighting method is employed to preprocess the array,thereby improving the efficiency of array optimization.Secondly,based on the density-weighted array,the Sobol sequence is introduced for population initialization,followed by the application of the EEFO algorithm to further optimize the positions of the array elements in search of the global optimum.Finally,to overcome the limitation of the asymmetric matrix mapping method which transforms the problem of solving actual distances into solving two mapping matrices,Gaussian perturbation is applied to the optimized array to ful-ly enhance the array's degrees of freedom.The experimental results confirm that the proposed method significantly re-duces the computational overhead of the optimization process,enhances the degrees of freedom of the array,and achieves effective suppression of the peak sidelobe level.

陈玉锋;龙伟军;何洋洋;徐艺卓;郝振中

南京信息工程大学电子与信息工程学院,江苏 南京 210044南京信息工程大学电子与信息工程学院,江苏 南京 210044海洋装备电磁效应及安全全国重点实验室,湖北 武汉 430064||中国舰船研究设计中心,湖北 武汉 430064南京信息工程大学电子与信息工程学院,江苏 南京 210044南京信息工程大学电子与信息工程学院,江苏 南京 210044

信息技术与安全科学

阵列天线SEEFO算法峰值旁瓣电平高斯扰动阵元自由度

array antennaSobol electric eel foraging optimization(SEEFO)algorithmpeak sidelobe level(PSLL)Gaussian perturbationelement distribution freedom

《雷达科学与技术》 2026 (2)

140-147,154,9

国家自然科学基金(62071440)

10.3969/j.issn.1672-2337.2026.02.003

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