论文标题
从稀疏的波束形成图中的自动源定位和光谱生成
Automatic source localization and spectra generation from sparse beamforming maps
论文作者
论文摘要
波束形成是一种研究空气声现象的成像工具，并通过整合感兴趣的空间区域而导致高维数据，从而将其分解为光谱。本文提出了两种方法，可以在稀疏的光束图中自动识别空气声源，并提取其相应光谱，以克服对感兴趣区域的手动定义。在两个缩放的机身半模型风能测量和通用单极源上评估该方法。第一个依赖于稀疏波束形成地图中航空声宽带源的空间正态分布。第二种使用分层聚类方法。这两种方法都可以稳健地对统计噪声，并根据源的存在，位置和空间概率估计，以自动确定感兴趣区域的存在。
Beamforming is an imaging tool for the investigation of aeroacoustic phenomena and results in high dimensional data that is 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.
