基于鸡群优化算法的二维MUSIC谱峰搜索算法  

Two-dimensional MUSIC Spectral Peak Search Algorithm Based on Chicken Swarm Optimization

作　　者：窦慧晶 朱子云 高立菁 DOU Huijing;ZHU Ziyun;GAO Lijing(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学信息学部,北京100124

出　　处：《北京工业大学学报》2018年第11期1409-1413,共5页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(61171137);北京市教育委员会科技计划资助项目(KM201210005001)

摘　　要：针对二维多重信号分类(multiple signal classification,MUSIC)算法在进行波达方向(direction of arrival,DOA)估计时计算速度慢、运算复杂度高的缺点,提出基于鸡群算法的二维MUSIC谱峰搜索算法.该算法将鸡群算法与MUSIC算法相结合,在谱峰搜索部分应用鸡群算法优化,利用鸡群算法寻优能力强的优点,快速搜索出谱峰所对应的角度.仿真实验表明,鸡群算法能有效克服谱峰搜索中出现的计算量大、计算复杂度高等问题,通过与其他仿生算法相比较,鸡群算法具有更快的收敛性、更强的稳定性以及更好的精确度.Since the two-dimensional MUSIC algorithm has a large amount of computation and slow computation when estimating the angle of arrival, a two-dimensional MUSIC spectral peak search algorithm was proposed based on chicken swarm optimization（ CSO）. This optimization combined the chicken swarm algorithm with the MUSIC algorithm and the CSO algorithm was applied to the spectral search part,and the angle corresponding to the spectral peaks was searched rapidly with a better searching capability of CSO. Simulation experiments show that the CSO can effectively overcome the computational complexity and huge volume of computation complexity in spectral peak search and by comparing with other bionic algorithms,the CSO has quicker convergence,greater stability and better accuracy.

关 键 词：波达方向(DOA)估计 多重信号分类(MUSIC)算法 鸡群优化 谱峰搜索 群智能算法 

分 类 号：TN911[电子电信—通信与信息系统]