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基于遗传模拟退火算法的子阵级自适应波束形成
刘子敬,陈曦,施庆展,崔开博
0
(国防科技大学 CEMEE国家重点实验室,长沙 410073)
摘要:
在阵列天线信号处理中,采用子阵级自适应波束形成技术可以降低算法复杂度和系统成本。针对子阵级自适应波束形成中最优子阵划分问题,提出了一种基于遗传模拟退火算法的阵列天线子阵最优划分方法。通过线性约束最小方差算法计算不同子阵划分下的自适应权矢量,形成自适应天线方向图,以方向图的最大旁瓣电平比作为成本函数,迭代优化得到最大旁瓣电平最优的子阵划分形式。仿真结果表明,使用该方法得到的非均匀子阵划分阵列天线其自适应方向图最大旁瓣电平能降低至-19 dB。通过对比分析不同子阵划分形式以及不同规模阵面的自适应方向图验证了该方法的可行性与有效性。
关键词:  阵列天线  子阵划分  自适应波束形成  遗传模拟退火
DOI:10.20079/j.issn.1001-893x.230915003
基金项目:国家自然科学基金资助项目(62301570)
Subarray Adaptive Beamforming Based on Genetic Simulated Annealing Algorithm
LIU Zijing,CHEN Xi,SHI Qingzhan,CUI Kaibo
(State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System,National University of Defense Technology,Changsha 410073,China)
Abstract:
In array antenna signal processing,subarray-level adaptive beamforming technology can reduce the complexity of the algorithm and system cost.For the optimal subarray division problem in subarray-level adaptive beamforming,an optimal subarray division method for array antenna based on genetic simulated annealing(GSA) algorithm is proposed.The algorithm calculates the adaptive weight vectors under different subarray divisions by the linear constrained minimum variance algorithm,forms the adaptive antenna pattern,and then iteratively optimizes the subarray divisions to get the optimal subarray division of the largest paraflap level by using the maximum paraflap level ratio of the antenna pattern as the cost function.The subarray division is optimized iteratively to obtain the optimal subarray division with maximum paraflap level.The simulation results show that the maximum paraflap level of the adaptive antenna pattern of the non-uniform subarray division array antenna obtained by this method can be reduced to -19 dB,and the feasibility and validity of the method are verified by comparing and analyzing the adaptive antenna patterns of different subarray divisions and different sizes of arrays.
Key words:  array antenna  subarray division  adaptive beamforming  genetic simulated annealing algorithm