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一个连贯的宽带声源定位使用一个统一的圆形阵列.
Meng Jiang1, Chibuzo Joseph Nnonyelu1, Jan Lundgren1
1Sensible Things that Communicate Research Centre, Mid Sweden University, 852 30 Sundsvall, Sweden.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了一种修改的阵列多重插值 (AMI) 方法用于声音源本地化,显著降低了计算复杂性. 新方法提高了准确性,并使低端处理器上的宽带处理成为可能.
科学领域:
- 声学 声学 在声学方面
- 信号处理 信号处理
- 机器人技术 机器人技术 机器人技术
背景情况:
- 复杂的应用,如机器人和自动驾驶汽车,需要高效的声音源定位.
- 现有的方法,如数组多重插值 (AMI) 与多重信号分类 (MUSIC),提供高精度,但存在高计算复杂性.
- 有需要的声音源本地化技术,平衡准确性与减少计算负载的资源有限的系统.
研究的目的:
- 为统一的圆形数组 (UCAs) 开发一个修改的数组多重插值 (AMI) 方法,该方法可以降低计算复杂性,同时保持高音源本地化精度.
- 调查UCA特定的聚焦矩阵在消除贝塞尔函数计算以减少复杂性的有效性.
- 将拟议方法的性能与iMusic,WS-TOPS和原始AMI等现有算法进行比较.
主要方法:
- 提出了一个修改的数组多重插值 (AMI) 技术,专门为统一的圆形数组 (UCAs) 量身定制.
- 引入了UCA特定的聚焦矩阵,以消除对贝塞尔函数计算的需求,从而减少计算复杂性.
- 在各种场景下,进行了模拟,以将拟议的算法与iMusic,WS-TOPS和原始AMI进行比较.
主要成果:
- 拟议的修改后的AMI方法表明,与原始AMI相比,计算复杂性降低了.
- 该算法在不同实验场景中实现了声音源本地化估计准确度的提高.
- 与原来的AMI方法相比,观察到高达30%的计算时间减少.
- 开发的方法在准确性和速度方面表现出优于原始AMI的性能.
结论:
- 对UCAs的修改后的AMI有效地减少了音源定位的计算复杂性.
- 这一进步允许在低端微处理器上进行高精度,宽带阵列处理,使其适用于资源有限的应用程序.
- 拟议的方法为增强机器人,自动驾驶汽车和其他复杂系统中的声音源本地化能力提供了实际解决方案.
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