广西师范大学学报（自然科学版） ›› 2024, Vol. 42 ›› Issue (3): 131-140.doi: 10.16088/j.issn.1001-6600.2023082902

• 研究论文 • 上一篇    下一篇

基于改进帝王蝶算法的最大似然DOA估计

赵小梅, 丁勇*, 王海涛   

  1. 桂林电子科技大学 信息与通信学院, 广西 桂林 541004
  • 收稿日期:2023-08-29 修回日期:2023-10-16 发布日期:2024-05-31
  • 通讯作者: 丁勇(1977—), 男, 湖南祁阳人, 桂林电子科技大学讲师, 博士。E-mail: ding_yong@guet.edu.cn
  • 基金资助:
    广西创新驱动发展专项(桂科AA21077008); 广西人才与基地专项(桂科AD20297038); 广西高校中青年教师科研基础能力提升项目(2021KY0197)

Maximum Likelihood DOA Estimation Based on Improved Monarch Butterfly Algorithm

ZHAO Xiaomei, DING Yong*, WANG Haitao   

  1. School of Information and Communication, Guilin University of Electronic Technology, Guilin Guangxi 541004, China
  • Received:2023-08-29 Revised:2023-10-16 Published:2024-05-31

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摘要： 针对传统最大似然波达方向(maximum likelihood direction of arrival,ML-DOA)估计存在计算量大、估计精度差等问题,本文提出一种采用改进帝王蝶优化算法(improved monarch butterfly optimization algorithm,IMBO)的ML-DOA估计方法。IMBO算法通过精英反向学习策略对初始帝王蝶种群进行优化,得到适应度值较优的初始帝王蝶个体,进而能够改善帝王蝶种群的多样性;引入差分进化算法启发的变异操作以及自适应策略对帝王蝶个体的寻优方式进行改进,扩大了算法的搜索空间;引入了高斯-柯西变异算子,自适应调整变异步长,避免算法陷入局部最优。将IMBO应用于ML-DOA,实验表明,与传统的DOA估计算法相比,在不同信源数目、信噪比以及种群数量下,本文提出的算法收敛性能更好,均方根误差更低,运算量更小。

关键词: 波达方向, 最大似然估计, 帝王蝶算法, 精英反向学习, 自适应策略, 变异算子

Abstract: In response to the problems of excessive computational complexity and poor accuracy in traditional maximum likelihood direction of arrival (ML-DOA) estimation, improved monarch butterfly optimization algorithm is proposed and applied to ML-DOA estimation. By using elite reverse learning strategies to optimize the initial monarch butterfly population and obtaining individuals with better fitness values, the diversity of the monarch butterfly population can be improved; In order to improve the individual optimization method, the mutation operation and adaptive strategy inspired by differential evolution are used for expanding the search scope of the algorithm; At the same time, the Gaussian-Cauchy mutation operator is introduced to adjust the mutation step size, preventing the algorithm from trapping in local optima, and apply IMBO to ML-DOA. Experiments show that the proposed algorithm has better convergence performance, lower root-mean-square deviation and less computation under different number of sources, SNR and population, compared with the conventional DOA estimation algorithm.

Key words: direction of arrival, maximum likelihood estimation, butterfly optimization algorithm, elite reverse learning, adaptive strategy, mutation operator

中图分类号:  TN911.7

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