Yoon Seunghyun, Yang Haesang, Seong Woojae
Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
J Acoust Soc Am. 2021 Mar;149(3):1454. doi: 10.1121/10.0003603.
The sensitivity of underwater propagation models to acoustic and environmental variability increases with the signal frequency; therefore, realizing accurate acoustic propagation predictions is difficult. Owing to this mismatch between the model and actual scenarios, achieving high-frequency source localization using model-based methods is generally difficult. To address this issue, we propose a deep learning approach trained on real data. In this study, we focused on depth estimation. Several 18-layer residual neural networks were trained on a normalized log-scaled spectrogram that was measured using a single hydrophone. The algorithm was evaluated using measured data transmitted from the linear frequency modulation chirp probe (11-31 kHz) in the shallow-water acoustic variability experiment 2015. The signal was received through two vertical line arrays (VLAs). The proposed method was applied to all 16 sensors of the VLA to determine the estimation performance with respect to the receiver depth. Furthermore, frequency-difference matched field processing was applied to the experimental data for comparison. The results indicate that ResNet can determine complicated features of high-frequency signals and predict depths, regardless of the receiver depth, while exhibiting robust environmental and positional variability.
水下传播模型对声学和环境变化的敏感性随信号频率增加而增强，因此，实现准确的声学传播预测颇具难度。由于模型与实际场景之间存在这种不匹配，利用基于模型的方法实现高频源定位通常较为困难。为解决这一问题，我们提出一种基于真实数据训练的深度学习方法。在本研究中，我们聚焦于深度估计。在使用单个水听器测量得到的归一化对数缩放频谱图上训练了多个18层残差神经网络。该算法利用2015年浅海水声变化实验中线性调频脉冲探测器（11 - 31kHz）发射的测量数据进行评估。信号通过两个垂直线列阵（VLA）接收。将所提方法应用于VLA的所有16个传感器，以确定相对于接收器深度的估计性能。此外，对实验数据应用频差匹配场处理进行比较。结果表明，残差网络（ResNet）能够确定高频信号的复杂特征并预测深度，无论接收器深度如何，同时展现出强大的环境和位置变化适应性。
