There is a significant need for precise and reliable forecasting of the far-field noise emanating from shipping vessels. Conventional full-order models based on the Navier-Stokes equations are unsuitable, and sophisticated model reduction methods may be ineffective for accurately predicting far-field noise in environments with seamounts and significant variations in bathymetry. Recent advances in reduced-order models, particularly those based on convolutional and recurrent neural networks, offer a faster and more accurate alternative. These models use convolutional neural networks to reduce data dimensions effectively. However, current deep-learning models face challenges in predicting wave propagation over long periods and for remote locations, often relying on auto-regressive prediction and lacking far-field bathymetry information. This research aims to improve the accuracy of deep-learning models for predicting underwater radiated noise in far-field scenarios. We propose a novel range-conditional convolutional neural network that incorporates ocean bathymetry data into the input. By integrating this architecture into a continual learning framework, we aim to generalize the model for varying bathymetry worldwide. To demonstrate the effectiveness of our approach, we analyze our model on several test cases and a benchmark scenario involving far-field prediction over Dickin's seamount in the Northeast Pacific. Our proposed architecture effectively captures transmission loss over a range-dependent, varying bathymetry profile. This architecture can be integrated into an adaptive management system for underwater radiated noise, providing real-time end-to-end mapping between near-field ship noise sources and received noise at the marine mammal's location.
Wind speed retrieval at sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologies, especially satellite sensors, provide complementary means to monitor wind speed. As sea surface winds produce sounds that propagate underwater, underwater acoustics recordings can also deliver fine-grained wind-related information. Whereas model-driven schemes, especially data assimilation approaches, are the state-of-the-art schemes to address inverse problems in geoscience, machine learning techniques become mo…
Direct localization (DLOC) methods, which use the observed data to localize a source at an unknown position in a one-step procedure, generally outperform their indirect two-step counterparts (e.g., using time-difference of arrivals). However, underwater acoustic DLOC methods require prior knowledge of the environment, and are computationally costly, hence slow. We propose, what is to the best of our knowledge, the first data-driven DLOC method. Inspired by classical and contemporary optimal model-based DLOC solutions, and leveraging the capabilities of convolutional neural networks (CNNs), we …
Vel Tech University（Vel Tech大学）；Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）
In underwater acoustic sensor networks (UWASN), energy-reliable data transmission is a challenging task. This is due to acoustic transmission disturbances caused by excessive noise, exceptionally long propagation delays, a high bit error rate, limited bandwidth capability, and interference. One of the most important issues of UWASN for research is how to extend the life span of data transmission. Data transfer from a source node to a destination node in UWASN is a complicated topic for researchers. Many routing algorithms, such as vector base forwarding and depth base routing, have been develo…
Advancing Robust Underwater Acoustic Target Recognition through Multi-task Learning and Multi-Gate Mixture-of-Experts
Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）；University of Chinese Academy of Sciences（中国科学院大学）；State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）
Adversarial multi-task underwater acoustic target recognition: towards robustness against various influential factors
Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所语言声学与内容理解重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所声场声信息国家重点实验室）
Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）；Thales Solutions Asia Pte Ltd（泰雷兹解决方案亚洲私人有限公司）
Underwater environments create a challenging channel for communications. In this paper, we design a novel receiver system by exploring the machine learning technique--Deep Belief Network (DBN)-- to combat the signal distortion caused by the Doppler effect and multi-path propagation. We evaluate the performance of the proposed receiver system in both simulation experiments and sea trials. Our proposed receiver system comprises of DBN based de-noising and classification of the received signal. First, the received signal is segmented into frames before the each of these frames is individually pre…
Joint Source-Environment Adaptation of Data-Driven Underwater Acoustic Source Ranging Based on Model Uncertainty
Interdisciplinary Health Sciences Institute (IHSI), University of Illinois Urbana-Champaign（跨学科健康科学研究院（IHSI），伊利诺伊大学厄巴纳-香槟分校）；Acoustic Research Laboratory, National University of Singapore, Tropical Marine Science Institute（声学研究实验室，新加坡国立大学，热带海洋科学研究所）；Department of Electrical and Computer Engineering, Stony Brook University（电气与计算机工程系，石溪大学）
Hankel-FNO: Fast Underwater Acoustic Charting Via Physics-Encoded Fourier Neural Operator
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China（信息科学与电子工程学院，浙江大学，杭州，中国）；Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, USA（Scripps 海洋研究所，加州大学圣地亚哥分校，La Jolla，加利福尼亚州 92093，美国）
