Beamforming is an imaging tool for the investigation of aeroacoustic phenomena and results in high-dimensional data that are broken down to spectra by integrating spatial regions of interest. This paper presents two methods that enable the automated identification of aeroacoustic sources in sparse beamforming maps and the extraction of their corresponding spectra to overcome the manual definition of regions of interest. The methods are evaluated on two scaled airframe half-model wind tunnel measurements and on a generic monopole source. The first relies on the spatial normal distribution of aeroacoustic broadband sources in sparse beamforming maps. The second uses hierarchical clustering methods. Both methods are robust to statistical noise and predict the existence, location, and spatial probability estimation for sources based on which regions of interest are automatically determined.

中文翻译：

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从稀疏波束形成图中自动进行源定位和光谱生成

波束成形是一种用于研究气动声学现象的成像工具，可生成高维数据，通过整合感兴趣的空间区域将其分解为频谱。本文提出了两种方法，可以在稀疏波束形成图中自动识别气动声源，并提取其相应的光谱，以克服手动定义感兴趣区域的问题。这些方法是在两个按比例缩放的机身半模型风洞测量和通用单极源上进行评估的。第一个依赖于稀疏波束形成图中气动声学宽带源的空间正态分布。第二种使用层次聚类方法。这两种方法对统计噪声都有鲁棒性，可以预测存在、位置、