| 引用本文: | 曹孟华,李龙,谢红卫.改进遗传算法在传声器阵列优化中的应用.[J].国防科技大学学报,2019,41(6):126-134.[点击复制] |
| CAO Menghua,LI Long,XIE Hongwei.Application of improved genetic algorithm in microphone array optimization[J].Journal of National University of Defense Technology,2019,41(6):126-134[点击复制] |
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| 改进遗传算法在传声器阵列优化中的应用 |
| 曹孟华1,李龙2,谢红卫1 |
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(1. 国防科技大学 智能科学学院, 湖南 长沙 410073;2. 国防科技大学 前沿交叉学科学院, 湖南 长沙 410073)
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| 摘要: |
| 规则平面阵列因其结构周期性,在进行波束综合时存在主瓣宽、旁瓣电平高等问题。对此,提出一种基于改进遗传算法的阵列优化方法。设计平面栅格传声器阵列,以满足阵元间距的要求,并构造以主瓣宽度为约束条件、以全局旁瓣电平为适应度的目标函数,对常规遗传算法进行改进,采取个体间自由交叉、随机的阵元数量强制变异的策略来增大种群的搜索范围。通过仿真,得到多个优化阵列,与几种规则平面阵列相比,在不同的信噪比输入下,经过改进遗传算法优化得到的随机阵列均有更好的表现。而相比于几种常规的优化算法,改进的遗传算法具有更强的搜索能力,得到数量更多、性能更优的随机阵列,由此证明了所提方法的可行性。 |
| 关键词: 遗传算法 平面栅格 全局旁瓣电平 主瓣宽度 |
| DOI：10.11887/j.cn.201906019 |
| 投稿日期：2018-04-11 |
| 基金项目: |
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| Application of improved genetic algorithm in microphone array optimization |
| CAO Menghua1, LI Long2, XIE Hongwei1 |
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(1. College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China;2. College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| Due to its periodic structure, the regular planar array has problems, such as wide beamwidth, high global side lobe in beam synthesis. So, an array optimization method based on the improved genetic algorithm was proposed. A planar grid microphone array was designed to satisfy the requirement of element spacing. An objective function with the main lobe width as the constraint and the global side lobe level as the fitness was constructed. The strategy of free intersection among individuals and random element number forced mutation was adopted to increase the searching range of the population on the basis of conventional genetic algorithms. A number of optimized arrays were obtained through simulation. Compared with several regular planar arrays, the random arrays optimized by the improved genetic algorithm have better performance under different signal-to-noise ratio inputs; compared with several conventional optimization algorithms, the improved genetic algorithm has stronger search ability, the number of random arrays is more, and the performance is better, which proves the feasibility of the proposed method. |
| Keywords: genetic algorithm planar grid global side lobe level beamwidth |
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