| 引用本文: | 陈玉锋，龙伟军，何洋洋，徐艺卓，郝振中. SEEFO算法驱动的多约束稀布阵列优化方法研究[J]. 雷达科学与技术, 2026, 24(2): 140-147.[点击复制] |
| CHEN Yufeng, LONG Weijun, HE Yangyang, XU Yizhuo, HAO Zhenzhong. Sparse Array Optimization Under Multiple Constraints Driven by the SEEFO[J]. Radar Science and Technology, 2026, 24(2): 140-147.[点击复制] |
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| 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。 |
| 关键词: 阵列天线 SEEFO算法 峰值旁瓣电平 高斯扰动 阵元自由度 |
| DOI：DOI:10.3969/j.issn.1672-2337.2026.02.003 |
| 分类号:TN820；TN957 |
| 基金项目:国家自然科学基金（62071440） |
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| Sparse Array Optimization Under Multiple Constraints Driven by the SEEFO |
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CHEN Yufeng, LONG Weijun, HE Yangyang, XU Yizhuo, HAO Zhenzhong
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1. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;2. National Key Laboratory of Electromagnetic Effect and Security on Marine Equipment, Wuhan 430064, China;3. China Ship Development and Design Center, Wuhan 430064, China
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| Abstract: |
| To address the array antenna optimization problem under multiple constraints, including a minimum element spacing constraint, a radiation pattern optimization method that integrates a Sobol sequence-based electric eel foraging 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 fully enhance the array’s degrees of freedom. The experimental results confirm that the proposed method significantly reduces the computational overhead of the optimization process, enhances the degrees of freedom of the array, and achieves effective suppression of the peak sidelobe level. |
| Key words: array antenna Sobol electric eel foraging optimization （SEEFO） algorithm peak sidelobe level （PSLL） Gaussian perturbation element distribution freedom |
