Document Type
Article
Publication Date
6-22-2022
Department
Department of Electrical and Computer Engineering
Abstract
Ice environments pose challenges for conventional underwater acoustic localization techniques due to theirmultipath 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.
Publication Title
Sensors
Recommended Citation
Whitaker, S.,
Barnard, A.,
Anderson, G.,
&
Havens, T. C.
(2022).
Through-Ice Acoustic Source Tracking Using Vision Transformers with Ordinal Classification.
Sensors,
22(13).
http://doi.org/10.3390/s22134703
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16216
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Publisher’s version of record: https://doi.org/10.3390/s22134703