基于蚁群算法的子阵级自适应多波束形成  

Adaptive multi-beamforming at subarray level based on ant colony algorithm

作　　者：张忠民[1] 李蔚然 ZHANG Zhongmin;LI Weiran(College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China)

机构地区：[1]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001

出　　处：《应用科技》2022年第1期83-89,共7页Applied Science and Technology

基　　金：中央高校基本科研业务费项目(3072021CF0816).

摘　　要：在大型阵列信号处理中,可以在子阵级进行数字波束扫描形成多波束,降低硬件成本和系统复杂度,其中子阵划分的优劣直接决定信号处理的性能。针对子阵级多波束形成的问题,提出了一种基于蚁群算法的子阵划分最优策略,该策略将蚂蚁的迁移路径作为子阵划分方案,以峰值旁瓣电平为优化目标进行迭代搜索,使得子阵级自适应形成多波束方向图的旁瓣性能达到最优。首先,分析了蚁群算法的基本原理,对子阵划分问题的解空间进行建模,设计最优策略并求解得子阵划分方案;然后采用线性约束最小方差准则(LCMV)计算子阵级权矢量,形成多波束方向图;最后通过对比分析了多波束方向图的性能。仿真实验证明了所提算法得出的子阵划分以及激励匹配方案的有效性。In large array signal processing,multi-beam can be formed by digital beam scanning at subarray level to reduce hardware cost and system complexity.The subarray division directly determines the performance of signal processing.Focusing on the problem of subarray level multi-beamforming,we propose an optimal subarray partition strategy based on ant colony algorithm.Firstly,the basic principle of the ant colony algorithm is analyzed and applied to the subarray division.The solution space of the subarray division configuration problem is modeled to solve the subarray division scheme.Then,the linear constraint minimum variance(LCMV)is used to calculate the subarray level weight vectors to form the multi-beam pattern.Finally,the performance of the multi-beam pattern is analyzed with the highest sidelobe level as the objective of optimization.Simulation experiments demonstrate the effectiveness of the subarray division optimal clustering and excitation matching solution obtained by the proposed algorithm.

关 键 词：多波束 蚁群算法 子阵 波束形成 大规模阵列 LCMV算法 旁瓣抑制 数字加权 

分 类 号：TN957.2[电子电信—信号与信息处理]