发表刊物：Applied Acoustics
关键字：Deep learning; Fine-tuning; Scattering; Rotating beamforming
摘要：Acoustic imaging of low-altitude aircraft in complex environments such as cabins, buildings, and mountains typically relies on beamforming, in which Green's functions are tailored to account for scattering, but at a high computational cost. To efficiently predict tailored Green's functions, a U-shaped convolutional neural network is designed to take physical parameters as input: two images of the real and imaginary parts of the source information, six images of the scatterer in orthographic projection, and two images of the real and imaginary parts of the incident wave field at the imaging plane. To enhance generalizability, the pre-trained network can be fine-tuned using some numerical data from out-of-training configurations. The developed approach reduces the time for scattering prediction from minutes to seconds and demonstrates accuracy on various scatterer shapes, both inside and outside the training datasets. Finally, the deep-learning-tailored Green's functions are applied to rotating beamforming in both numerical and experimental settings, yielding results comparable to those obtained with numerical Green's functions, along with an apparent reduction in computational time. This work highlights the potential of deep learning to accelerate beamforming in acoustically challenging environments.
合写作者：Jiahua He, Wangqiao Chen, Shengwu Chen
第一作者：Jiaming Zhou
论文类型：期刊论文
通讯作者：Lican Wang
学科门类：工学
一级学科：航空宇航科学与技术
文献类型：J
卷号：253
期号：111385
页面范围：1-10
是否译文：否
发表时间：2026-05-23
收录刊物：SCI、EI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0003682X26001660#s0065