KU Leuven, ESAT-DRAMCO, Ghent Technology Campus, 9000 Ghent, Belgium.
Sensors (Basel). 2019 Feb 21;19(4):900. doi: 10.3390/s19040900.
Advances in energy efficient electronic components create new opportunities for wireless acoustic sensor networks. Such sensors can be deployed to localize unwanted and unexpected sound events in surveillance applications, home assisted living, etc. This research focused on a wireless acoustic sensor network with low-profile low-power linear MEMS microphone arrays, enabling the retrieval of angular information of sound events. The angular information was wirelessly transmitted to a central server, which estimated the location of the sound event. Common angle-of-arrival localization approaches use triangulation, however this article presents a way of using angular probability density functions combined with a matching algorithm to localize sound events. First, two computationally efficient delay-based angle-of-arrival calculation methods were investigated. The matching algorithm is described and compared to a common triangulation approach. The two localization algorithms were experimentally evaluated in a 4.25 m by 9.20 m room, localizing white noise and vocal sounds. The results demonstrate the superior accuracy of the proposed matching algorithm over a common triangulation approach. When localizing a white noise source, an accuracy improvement of up to 114% was achieved.
节能电子元件的进步为无线声传感器网络创造了新的机会。这种传感器可以部署在监控应用、家庭辅助生活等领域，用于定位不需要和意外的声音事件。本研究专注于具有低轮廓、低功耗线性 MEMS 麦克风阵列的无线声传感器网络，能够获取声音事件的角度信息。角度信息通过无线传输到中央服务器，该服务器估计声音事件的位置。常见的到达角定位方法使用三角测量，但本文提出了一种使用角度概率密度函数结合匹配算法来定位声音事件的方法。首先，研究了两种计算效率高的基于延迟的到达角计算方法。描述了匹配算法，并与常见的三角测量方法进行了比较。在一个 4.25 米乘 9.20 米的房间中，对两种定位算法进行了实验评估，定位了白噪声和人声。结果表明，与常见的三角测量方法相比，所提出的匹配算法具有更高的准确性。在定位白噪声源时，精度提高了 114%。
