陶奎,王斌,田雪,等. 基于改进型灰狼优化算法和窗函数加权的稀布矩形平面阵列天线综合[J]. 电波科学学报,2024,39(3):1-10. DOI: 10.12265/j.cjors.2023174
      引用本文: 陶奎,王斌,田雪,等. 基于改进型灰狼优化算法和窗函数加权的稀布矩形平面阵列天线综合[J]. 电波科学学报,2024,39(3):1-10. DOI: 10.12265/j.cjors.2023174
      TAO K, WANG B, TIAN X, et al. Sparse rectangular planar array antenna synthesis based on improved grey wolf optimizer algorithm and window function weighting[J]. Chinese journal of radio science,2024,39(3):1-10. (in Chinese). DOI: 10.12265/j.cjors.2023174
      Citation: TAO K, WANG B, TIAN X, et al. Sparse rectangular planar array antenna synthesis based on improved grey wolf optimizer algorithm and window function weighting[J]. Chinese journal of radio science,2024,39(3):1-10. (in Chinese). DOI: 10.12265/j.cjors.2023174

      基于改进型灰狼优化算法和窗函数加权的稀布矩形平面阵列天线综合

      Sparse rectangular planar array antenna synthesis based on improved grey wolf optimizer algorithm and window function weighting

      • 摘要: 针对在阵列孔径、阵元数目、最小阵元间距等多约束条件下的稀布矩形平面阵列天线优化问题,提出了基于改进型灰狼优化(improved grey wolf optimizer, IGWO)算法和窗函数加权的稀布矩形平面阵列天线综合方法。首先,利用Tent混沌映射、非线性收敛因子、优势狼动态置信策略和对立学习策略对灰狼优化(grey wolf optimizer, GWO)算法进行改进,增加算法的种群多样性和跳出局部最优的能力。然后,利用窗函数对阵列单元进行加权,生成位置分布矩阵,减少稀疏矩阵优化时间,提高优化效率。最后,利用位置分布矩阵生成稀疏阵列,再运用IGWO算法进行多约束条件的稀布优化。为验证所提方法的有效性进行了仿真实验,实验结果表明,本文方法可以有效提高阵列天线的性能,降低峰值旁瓣电平,对于解决在多约束条件下的阵列分布问题,具有一定的工程意义和参考价值。

         

        Abstract: Aiming at the optimization problem of sparsely distributed rectangular planar array antenna under the constraints of array aperture, number of array elements and minimum array element spacing, a synthesis method of sparsely distributed rectangular planar array antenna based on improved grey wolf optimizer (IGWO) algorithm and window function weighting is proposed. Firstly, the grey wolf optimizer (GWO) algorithm is improved by Tent chaotic mapping, nonlinear convergence factor, dominant wolf dynamic confidence strategy and opposition-based learning strategy to increase the population diversity and the ability of the algorithm to jump out of the local optimal. Then, the window function is used to weight the array elements to generate the location distribution matrix, which reduces the time of sparse matrix optimization and improves the optimization efficiency. Finally, the location distribution matrix is used to generate sparse array, and then the IGWO algorithm is used to optimize the thinly distributed under multiple constraints. Simulation experiments are carried out to verify the effectiveness of the proposed method. The experimental results show that the proposed method can effectively improve the performance of the array antenna and reduce the peak sidelobe level. This integrated method is of engineering significance and reference value for solving array distribution problems under multiple constraints.

         

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