稀布线阵双维度嵌套优化方法

    Two-dimensional Nested Optimization Method for Sparse Linear Array

    • 摘要: 稀布阵和稀疏阵设计方法的研究表明,该类方法能够有效抑制栅瓣现象,并可以节省成本、降低系统复杂度。针对均匀线阵中出现的栅瓣现象,文中提出了一种包含阵元双维度空间信息的阵列模型,通过添加二维扰动,将阵元分布区域由阵轴方向扩展至阵轴法向,以削弱阵元最小间距约束带来的局限性,扩大阵元位置参数的搜索空间,并利用元启发式算法构建阵元空间位置参数嵌套优化框架,交替优化阵元的二维空间信息,增强阵列的非周期性,从而抑制了栅瓣的产生。实验结果表明,在给定阵列孔径和阵元数且对应均匀线阵出现栅瓣现象的情况下,较仅在阵轴方向优化的标准遗传算法和基本粒子群算法,所提方法的峰值旁瓣电平可降低3.04 dB。

       

      Abstract: The study on sparse array and sparse array design methods show that this type of methods can effectively suppress grating lobes phenomenon, save cost, and reduce system complexity. Aiming at the phenomenon of grating lobes observed in uniform linear arrays, an array model incorporating two-dimensional spatial information of array elements is proposed in this paper. By introducing two-dimensional perturbations, the spatial distribution of array elements is extended from the array axis direction to the array axis normal direction, mitigating the constraints imposed by the minimum element spacing and expanding the search space for element position parameters. An iterative optimization framework for element spatial position parameters is constructed using a metaheuristic algorithm, alternately optimizing the two-dimensional spatial information of array elements to enhance the non-periodicity of the array and suppress the generation of grating lobes. Experimental results demonstrate that, under the conditions of a given array aperture, element count, and the occurrence of grating lobes in a corresponding uniform linear array, the proposed approach reduces the peak sidelobe level by 3.04 dB compared to the standard genetic algorithm and the basic particle swarm optimization optimized only in the array axis direction.

       

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