Univ. Grenoble Alpes, CNRS, Grenoble-INP, GIPSA-Lab, 38000 Grenoble, France.
Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69100, LYON, France
JASA Express Lett. 2021 Jul;1(7):076001. doi: 10.1121/10.0005513.
In many acoustic imaging applications, conventional beamforming (CBF) cannot provide both accurate position and source level estimates simultaneously. Also, the CBF acoustic maps suffer from many artifacts due to the spreading of large point-spread-functions. An original CLEAN deconvolution procedure, including an additional plane containing out-of-plane interfering sources, is proposed here to achieve simultaneous localization, source level estimation, and de-noising. The approach is illustrated using experimental data mimicking a challenging deep-sea mining configuration: an underwater acoustic source of interest is located 700 m below the sea surface, tens of meters from a 3 m-length array, with boat noise as the disturbing source.
在许多声学成像应用中，传统波束形成（CBF）不能同时提供准确的位置和源级估计。此外，由于大的点扩散函数的扩展，CBF 声图会产生许多伪影。本文提出了一种原始的 CLEAN 反卷积方法，包括一个额外的平面，其中包含了来自非平面干扰源的信息，以实现同时定位、源级估计和去噪。该方法使用模拟深海采矿配置的实验数据进行说明：水下感兴趣的声源位于海面以下 700 米处，距离 3 米长的阵列有几十米远，干扰源是船只噪声。
