摘要
针对传统MUSIC算法运算量过大以及低信噪比下分辨率差的问题,提出将改进人工鱼群算法与MUSIC的谱峰搜索相结合,利用鱼群觅食和追逐来对解空间进行高效搜索,从而保证算法收敛的快速性和全局性.聚群的存在促使少量陷于局部最优解的人工鱼向着全局最优解的方向靠拢,提高了鱼群对不利环境的自适应性,也增强了算法的稳定性.与此同时,改进人工鱼群算法在一定程度上加快了后期收敛速度,提高了算法的估计性能.实验结果表明在低信噪比时方法相较于MUSIC而言具有更好的估计性能,并且大大减少了运算量,保证了算法的实时性.
The traditional MUSIC algorithm has the disadvantage of excessive computation and poor resolution at low SNR in DOA estimation.This paper proposes a method combining the improved artificial fish swarm algorithm with peak search.The predation and chase search of the fish stocks reflects the rapid and global characteristics of the algorithm convergence.The clustering of fish is an important force to avoid fish trapping in the local optimal solution,and it also provides protection for the self-adaptability of the fish and the stability of the algorithm.At the same time,the improved fish swarm algorithm accelerates the convergence speed to a certain extent and improves the estimation performance of the algorithm.The results of simulation show that the algorithm has better estimation performance than MUSIC at low SNR,and weakens the influence of computation.
作者
张明星
王鹏
白艳萍
侯宇超
ZHANG Ming-xing;WANG Peng;BAI Yan-ping;HOU Yu-chao(School of Science,North University of China,Taiyuan 030051,China)
出处
《数学的实践与认识》
北大核心
2019年第22期163-170,共8页
Mathematics in Practice and Theory
基金
国家自然科学基金(61774137)
山西省自然科学基金(201801D121026,201701D121012,201701D221121)
山西省回国留学人员科研项目(2016-088)
关键词
MUSIC算法
人工鱼群算法
改进人工鱼群算法
DOA估计
MUSIC algorithm
artificial fish swarm algorithm
improved artificial fish swarm algorithm
DOA estimation
