文章摘要
邵元,杨超,郭辉.基于鸡群算法的近场声源三维定位MUSIC算法[J].声学技术,2020,39(5):638~643
基于鸡群算法的近场声源三维定位MUSIC算法
Chicken swarm optimization based MUSIC algorithm for three-dimensional localization of near-field sound source
投稿时间:2019-06-10  修订日期:2019-07-11
DOI:10.16300/j.cnki.1000-3630.2020.05.019
中文关键词: 多重信号分类  近场  声源定位  鸡群算法
英文关键词: multiple signal classification (MUSIC)  near-field  sound source localization  chicken swarm optimization (CSO)
基金项目:新能源汽车振动噪声测试与控制专业技术服务平台(18DZ2295900)、国家自然科学基金资助项目(51675324)
作者单位E-mail
邵元 上海工程技术大学 机械与汽车工程学院, 上海 201600  
杨超 上海工程技术大学 机械与汽车工程学院, 上海 201600 nfuyangchao@sina.com 
郭辉 上海工程技术大学 机械与汽车工程学院, 上海 201600  
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中文摘要:
      针对三维多重信号分类(Multiple Signal Classification,MUSIC)算法估计声源位置时计算速度慢,计算量大等缺点,提出了一种基于鸡群优化(Chicken Swarm Optimization,CSO)算法的近场声源三维定位算法。首先建立近场声源信号接收的数学模型,并选取三维MUSIC算法中的空间谱函数为文章算法中的适应度函数。通过不断迭代和局部搜索,以适应度值为指标对鸡群个体进行排序,最终得到最优鸡群个体的位置,即近场待测声源的坐标。仿真和实验结果表明:文中算法具有定位精度高、计算效率高、实时性好等优点,文中算法的平均用时仿真时为三维MUSIC算法平均用时的1.9%,实验时为三维MUSIC算法用时的3.2%。
英文摘要:
      The three-dimensional multiple signal classification (MUSIC) algorithm has the disadvantages of slow calculation speed and large calculation amount in estimating the position of sound source. In this paper, a three-dimensional algorithm for the localization of near-field sound source is proposed based on the chicken swarm optimization (CSO) algorithm. Firstly, the receiving model of near-field signal is established, and the spatial spectrum function in the MUSIC algorithm is selected as the fitness function. Through continuous iteration and local search, the chicken individual is sorted continuously with the fitness value as the index, and finally the position of the optimal chicken individual, which is the coordinates of the near-field sound source to be measured, is obtained. The results of simulation and experiment show that the proposed algorithm has high localization accuracy, high calculation efficiency and good real- time performance compared with the three-dimensional MUSIC algorithm; the average time for three-dimensional localization of sound source is only 1.9 % of that of the three-dimensional MUSIC algorithm in simulation and 3.2% in experiment.
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