Semi-supervised graph learning for underwater source localization using ship-of-opportunity spectrograms.

IF 2.3 2区 物理与天体物理 Q2 ACOUSTICS
Jhon A Castro-Correa, Mohsen Badiey, Jhony H Giraldo, Fragkiskos D Malliaros
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引用次数: 0

Abstract

Conventional techniques for underwater source localization have traditionally relied on optimization methods, matched-field processing, beamforming, and, more recently, deep learning. However, these methods often fall short to fully exploit the data correlation crucial for accurate source localization. This correlation can be effectively captured using graphs, which consider the spatial relationship among data points through edges. This work introduces a novel graph learning module for source localization using spectrograms from ships-of-opportunity, which represent mid-frequency acoustic broadband signals from ship-radiated noise ranging from 360 to 1100 Hz, collected during the 2017 Seabed Characterization Experiment (SBCEX 2017). The proposed approach follows a two-step process: first, a pre-trained convolutional neural network (CNN) module is used for feature extraction via self-supervised learning, and then a graph neural network model is trained using semi-supervised learning for source localization. The graph is constructed using a k-nearest neighbor algorithm, incorporating features extracted by the CNN from the spectrograms. By employing this two-stage training strategy, our framework addresses the challenge of limited labeled data availability while achieving performance comparable to conventional supervised learning models. The effectiveness of our approach is demonstrated through model evaluation on both synthetic and measured data, showcasing the architecture's ability to generalize well to unseen scenarios.

利用机会船谱图进行水下源定位的半监督图学习。
传统的水下源定位技术传统上依赖于优化方法、匹配场处理、波束形成以及最近的深度学习。然而,这些方法往往不能充分利用对准确定位源至关重要的数据相关性。使用图可以有效地捕获这种相关性,图通过边缘考虑数据点之间的空间关系。这项工作引入了一种新的图形学习模块,用于使用机会船的频谱图进行源定位,这些频谱图代表了2017年海底表征实验(SBCEX 2017)期间收集的360至1100hz范围内的船舶辐射噪声的中频声宽带信号。该方法分为两步:首先,使用预训练的卷积神经网络(CNN)模块通过自监督学习进行特征提取,然后使用半监督学习训练图神经网络模型进行源定位。该图使用k近邻算法构建,并结合了CNN从谱图中提取的特征。通过采用这种两阶段训练策略,我们的框架解决了有限标记数据可用性的挑战,同时实现了与传统监督学习模型相当的性能。我们的方法的有效性是通过对合成数据和测量数据的模型评估来证明的,展示了体系结构的能力,可以很好地推广到未知的场景。
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来源期刊
CiteScore
4.60
自引率
16.70%
发文量
1433
审稿时长
4.7 months
期刊介绍: Since 1929 The Journal of the Acoustical Society of America has been the leading source of theoretical and experimental research results in the broad interdisciplinary study of sound. Subject coverage includes: linear and nonlinear acoustics; aeroacoustics, underwater sound and acoustical oceanography; ultrasonics and quantum acoustics; architectural and structural acoustics and vibration; speech, music and noise; psychology and physiology of hearing; engineering acoustics, transduction; bioacoustics, animal bioacoustics.
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