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移动声源定位和跟踪,用于配备自旋转双麦克风阵列的自主机器人

Deepak Gala¹, Liang Sun²

¹Klipsch School of Electrical and Computer Engineering, New Mexico State University, Las Cruces, New Mexico 88001, USA.

The Journal of the Acoustical Society of America

|August 29, 2023

概括

本研究介绍了使用旋转双麦克风系统在3D空间中跟踪移动声音源的两种方法. 扩展的卡尔曼过器和希尔伯特变换有效地识别声音源位置和运动特征.

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:

在三维 (3D) 空间中精确地定位和跟踪声音源对于各种应用,包括机器人和音频分析至关重要.
传统方法经常面临移动源和复杂环境的挑战.

研究的目的:

开发和评估用于定位和跟踪在3D空间中移动的声音源的新方法.
为了应对旋转双麦克风系统和移动声音源所带来的复杂性.

主要方法:

开发了两种不同的方法:一种是利用基于四个状态空间模型的扩展卡尔曼波器 (EKF),另一种是使用基于希尔伯特变换的方法.
双麦克风系统捕获声音,产生一个侧边形的频道间距离差 (ICDD) 信号,受源和阵列运动的影响.
EKF 识别了 ICDD 信号的振幅和相位,而希尔伯特方法则将分析 ICDD 信号与局部化的参考信号进行比较.

主要成果:

在状态空间模型上进行了可观测性分析,以了解EKF性能限制.
希尔伯特转换法,结合移动平均线过器,有效地减少噪音和音源定位中的工件.
模拟研究在对比研究中证明了两种拟议方法的有效性.

结论:

提出的扩展卡尔曼波器和基于希尔伯特变换的方法为3D声音源定位和跟踪提供了可行的解决方案.
该研究强调了分析过器设计状态空间模型可观测性的重要性.
这些技术显示出增强基于音频的感知系统的前景.

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