Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI 49931, USA.
Graduate Program in Acoustics, The Pennsylvania State University, University Park, PA 16802, USA.
Sensors (Basel). 2022 Jun 22;22(13):4703. doi: 10.3390/s22134703.
Ice environments pose challenges for conventional underwater acoustic localization techniques due to their multipath and non-linear nature. In this paper, we compare different deep learning networks, such as Transformers, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Vision Transformers (ViTs), for passive localization and tracking of single moving, on-ice acoustic sources using two underwater acoustic vector sensors. We incorporate ordinal classification as a localization approach and compare the results with other standard methods. We conduct experiments passively recording the acoustic signature of an anthropogenic source on the ice and analyze these data. The results demonstrate that Vision Transformers are a strong contender for tracking moving acoustic sources on ice. Additionally, we show that classification as a localization technique can outperform regression for networks more suited for classification, such as the CNN and ViTs.
冰环境对传统的水下声定位技术构成挑战，因为它们具有多径和非线性特性。在本文中，我们比较了不同的深度学习网络，如 Transformer、卷积神经网络（CNN）、长短时记忆（LSTM）网络和 Vision Transformer（ViT），用于使用两个水下声矢量传感器对单个移动的冰上声源进行被动定位和跟踪。我们将有序分类作为一种定位方法，并将结果与其他标准方法进行比较。我们通过被动记录冰上人为声源的声信号来进行实验，并对这些数据进行分析。结果表明，Vision Transformer 是跟踪冰上移动声源的有力竞争者。此外，我们还表明，对于更适合分类的网络，如 CNN 和 ViT，分类作为一种定位技术可以优于回归。
