小生境粒子群算法在多信号DOA估计中的应用  

Application of Niche Particle Swarm Optimization in Multi-signal DOA Estimation

作　　者：朱任 庄铭杰 ZHU Ren;ZHUANG Mingjie(College of Engineering,Huaqiao University,Quanzhou 362021,China)

机构地区：[1]华侨大学工学院,福建泉州362021

出　　处：《电讯技术》2022年第4期473-481,共9页Telecommunication Engineering

基　　金：泉州市技术研究与开发项目(2018Z011)。

摘　　要：多重信号分类(Multiple Signal Classification,MUSIC)算法是波达方向(Direction of Arrival,DOA)估计领域中的经典算法之一,但其谱峰搜索过程的巨大计算量降低了算法的实时性。经典进化算法虽能降低搜索时间,却仅能搜索到一个解,当存在多个入射信号时便无法搜索全部解。为了解决该问题,在粒子群算法的基础上,借鉴小生境思想提出了小生境粒子群算法,利用顺序聚类算法将粒子划分到不同的小生境,并根据小生境的迭代数选择不同搜索策略,兼顾了搜索广度和深度。仿真结果表明,改进粒子群算法在进行多谱峰搜索时能大幅降低搜索时间并搜索到全部解,与同类算法相比具有更高的精度和较少设置参数,其精度可以达到10^(-3),用时可以达到网格搜索的1/7000,在基于MUSIC算法的多个信号DOA估计中有重要的应用价值。Multiple signal classification(MUSIC)algorithm is one of the classic algorithms in the field of direction of arrival(DOA)estimation.However,the huge amount of computation in searching spectral peaks reduces the real-time performance of MUSIC algorithm.Although the classical evolutionary algorithm can reduce search time,it can only search out one solution.When there are multiple signals,it cannot search out all solutions.In order to solve this problem,on the basis of particle swarm optimization(PSO),the Niche PSO(NPSO)is proposed based on the idea of niche.The particles are divided into different niches by sequential clustering algorithm,and different search strategies are selected according to the number of iterations of the niches,which takes into account both the breadth and depth of search.The simulation results show that NPSO can greatly reduce search time and search out all solutions.Compared with the similar algorithms,NPSO has higher accuracy and fewer parameters,its accuracy can reach 10^(-3),and the time can reach 1/7000 of the grid search.It has important application value in multi-signal DOA estimation based on MUSIC algorithm.

关 键 词：波达方向估计 多重信号分类算法 谱峰搜索 小生境粒子群算法(NPSO) 

分 类 号：TN911.7[电子电信—通信与信息系统] TN971[电子电信—信息与通信工程]