基于分子群改进海象优化算法的矢量水听器宽带信号DOA估计  

DOA Estimation of Wideband Signals from Vector Hydrophones Based on Molecular Group Improved Whale Optimization Algorithm

作　　者：董洁[1] 王立府 王鹏[1] DONG Jie;WANG Lifu;WANG Peng(School of Mathematics,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学数学学院,山西太原030051

出　　处：《测试技术学报》2024年第6期642-651,共10页Journal of Test and Measurement Technology

基　　金：国家自然科学基金资助项目(61774137);山西省基础研究计划资助项目(202103021224195,202103021224212,202103021223189,20210302123019);山西省回国留学人员科研资助项目(2020-104,2021-108,2022-149)。

摘　　要：针对矢量水听器阵列宽带信号波达方向(DOA)估计中存在误差大,求解精度不高等问题,提出一种将分子群改进的海象优化算法(MGIWaOA)与极大似然(ML)估计算法相结合的宽带信号估计方法。首先利用信号子空间变换算法构建聚焦矩阵,将宽带信号转换为窄带信号,然后建立基于ML的DOA估计模型,再通过MGIWaOA进行优化估计。仿真结果表明,该算法比海象优化算法、正弦余弦算法、麻雀优化算法、差分进化算法、遗传算法、粒子群算法的ML估计方法具有更快的收敛速度、更低的均方根误差和更稳健的性能。To address the challenges such as significant estimation errors and low resolution in the estima⁃tion of the direction of arrival(DOA)of vector hydrophone array broadband signal,this paper proposes a novel approach that combines the molecular group improved whale optimization algorithm(MGIWaOA)with maximum likelihood(ML)estimation.The method begins by utilizing the signal subspace transforma⁃tion algorithm to construct a focusing matrix,transforming broadband signals into narrowband signals.Subsequently,a ML-based DOA estimation model is established,and optimization is performed using the MGI-WaOA.Simulation results demonstrate that the proposed algorithm exhibits faster convergence,lower root mean square error,and more robust performance compared to ML estimation methods based on the whale optimization algorithm,sine cosine algorithm,sparrow optimization algorithm,differential evolution algorithm,genetic algorithm,and particle swarm optimization.

关 键 词：宽带信号 矢量水听器阵列 信号子空间变换 极大似然 混沌对立学习 海象优化算法 

分 类 号：TN911.7[电子电信—通信与信息系统]