Passive Source Ranging Using Residual Neural Network With One Hydrophone in Shallow Water

Lin, Yonggang; Zhu, Min; Wu, Yanqun; Zhang, Wen

doi:10.1109/icicsp50920.2020.9232070

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Upon deeper exploration of the underwater acoustic environment and advancements in computer science, machine learning has seen extensive utilization in detecting, classifying, and localizing underwater sound sources and targets in underwater acoustics due to its adaptability and predictive capabilities (Yang et al, 2020), Niu et al (2019), andLin et al (2020) leveraged ResNet for depth and distance estimation of target sound sources using a single hydrophone. Niu et al (2019) proposed a two-step prediction strategy, showing superior performance across various environments, slowly varying source levels, and conditions with a high signal-to-noise ratio.…”

Section: Introductionmentioning

confidence: 99%

Machine learning–based feature prediction of convergence zones in ocean front environments

Xu,

Zhang,

Wang

2024

Front. Mar. Sci.

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The convergence zone holds significant importance in deep-sea underwater acoustic propagation, playing a pivotal role in remote underwater acoustic detection and communication. Despite the adaptability and predictive power of machine learning, its practical application in predicting the convergence zone remains largely unexplored. This study aimed to address this gap by developing a high-resolution ocean front-based model for convergence zone prediction. Out of 24 machine learning algorithms tested through K-fold cross-validation, the multilayer perceptron–random forest hybrid demonstrated the highest accuracy, showing its superiority in predicting the convergence zone within a complex ocean front environment. The research findings emphasized the substantial impact of ocean fronts on the convergence zone’s location concerning the sound source. Specifically, they highlighted that in relatively cold (or warm) water, the intensity of the ocean front significantly influences the proximity (or distance) of the convergence zone to the sound source. Furthermore, among the input features, the turning depth emerged as a crucial determinant, contributing more than 25% to the model’s effectiveness in predicting the convergence zone’s distance. The model achieved an accuracy of 82.43% in predicting the convergence zone’s distance with an error of less than 1 km. Additionally, it attained a 77.1% accuracy in predicting the convergence zone’s width within a similar error range. Notably, this prediction model exhibits strong performance and generalizability, capable of discerning evolving trends in new datasets when cross-validated using in situ observation data and information from diverse sea areas.

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Section: Introductionmentioning

confidence: 99%

Machine learning–based feature prediction of convergence zones in ocean front environments

Xu,

Zhang,

Wang

2024

Front. Mar. Sci.

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“…U NDERWATER acoustic source localization is an active research topic which is gaining relevance due to ocean environment monitoring, navigation and related applications. Recent approaches rely on machine learning, due to the capability to achieve impressive performance with limited prior information (e.g., unknown ocean environment, sound speed profile, and/or seabed parameters) [1]- [7]. A deep neural network trained by supervised learning is an end-toend model, which can automatically extract useful features and conduct one specific task (e.g, source localization) guided by the labels (source locations).…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the insufficiency of labels is common in underwater acoustics scenarios. Most of the existing works concentrate on utilizing different state-of-the-art architectures of deep neural networks referenced from the field of computer science, such as generalized regression neural network (GRNN) [5] and residual neural network (ResNet) [6], [7], to study their source localization Part of this paper was presented at the IEEE SENSORS 2021, Virtual Conference, October 2021. The authors would like to acknowledge the Norwegian Research Council and the industry partners of the GAMES consortium at NTNU for financial support (Grant No.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Fused Underwater Acoustic Source Localization Based on Contrastive Predictive Coding

et al. 2022

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We propose a time-frequency fused underwater acoustic source localization method based on self-supervised learning with contrastive predictive coding. Firstly, two feature extractors are trained to solve the pretext task (predicting the future) based on the unlabeled acoustic signals in the time and frequency domains, respectively. Next, encoders with frozen parameters are taken from the trained feature extractors for extracting the high-level features in the time and frequency domains. During the training stage of the source localizer, features extracted by two encoders are concatenated together as a time-frequency fused feature vector and fed into a 3-layer multi-layer perceptron for solving the downstream task (source localization) based on a tiny labeled dataset. This method is assessed on the SWellEx-96 Experiment and compared with several alternative methods. The performance analysis confirms the promising performance of our proposed method.

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“…Recently, there have been several studies on underwater source localization based on ML using the supervised learning scheme [9][10][11][12][13][14][15][16][17]. The general approach of underwater source localization by supervised learning scheme is through the use of acoustic propagation simulation models to create a huge simulation dataset for covering the real scenario.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Based on Principal Component Regression for Underwater Source Localization by Deep Learning

et al. 2021

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Underwater source localization is an important task, especially for real-time operation. Recently, machine learning methods have been combined with supervised learning schemes. This opens new possibilities for underwater source localization. However, in many real scenarios, the number of labeled datasets is insufficient for purely supervised learning, and the training time of a deep neural network can be huge. To mitigate the problem related to the low number of labeled datasets available, we propose a two-step framework for underwater source localization based on the semi-supervised learning scheme. The first step utilizes a convolutional autoencoder to extract the latent features from the whole available dataset. The second step performs source localization via an encoder multi-layer perceptron trained on a limited labeled portion of the dataset. To reduce the training time, an interpretable feature selection (FS) method based on principal component regression is proposed, which can extract important features for underwater source localization by only introducing the source location without other prior information. The proposed approach is validated on the public dataset SWellEx-96 Event S5. The results show that the framework has appealing accuracy and robustness on the unseen data, especially when the number of data used to train gradually decreases. After FS, not only the training stage has a 95% acceleration but the performance of the framework becomes more robust on the receiver-depth selection and more accurate when the number of labeled data used to train is extremely limited.

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Passive Source Ranging Using Residual Neural Network With One Hydrophone in Shallow Water

Cited by 4 publications

References 11 publications

Machine learning–based feature prediction of convergence zones in ocean front environments

Machine learning–based feature prediction of convergence zones in ocean front environments

Time-Frequency Fused Underwater Acoustic Source Localization Based on Contrastive Predictive Coding

Feature Selection Based on Principal Component Regression for Underwater Source Localization by Deep Learning

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