Classification of anti-submarine warfare sonar targets using a deep neural network
Abstract
A well-known problem with modern anti-submarine warfare sonars with narrow beamwidths and wide frequency bandwidths, is the frequent occurence of false alarms, particularly in littoral environments. This increases the workload of sonar operators and also reduces the usefulness of automatic systems such as autonomous underwater vehicles, since their limited communication abilities hinder them from sharing large amounts of contacts. In this paper, deep learning is applied on sonar data with a high amount of false alarms. Even though the data set is quite small, it is shown that it is possible to train a deep neural network that outperform simple signal-to-noise ratio (SNR) thresholding. Due to the limited amount of available data, a simple augmentation scheme is employed to increase the training and validation data sets, by copying some of the training instances. This augmentation scheme is shown to significantly improve the results of the training.
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An augmented dataset for training an ensemble of CNNs to classify false alarms from sonar data obtained in a nearshore environment showed unquestionable improvement compared with thresholding a signal-to-noise ratio of detections.
“The results presented show an unquestionable improvement in the classification of false alarms, in comparison with the usual method of thresholding a signal-to-noise ratio of the detections.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [139]
Much work in underwater acoustic data classification is conducted using synthetic data only or on a limited set of real data augmented with semisynthetic examples for training.
“Therefore, much work in this area is conducted using synthetic data only [124], or on a limited set of real data augmented with semisynthetic examples for training [62,128,139].”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [139]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
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Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · Sensors · doi:10.3390/s22062181 · Cited by 93 · see citing works
“An augmented dataset was introduced in [139] for training an ensemble of CNNs to classify false alarms from sonar data obtained in a nearshore environment.”
BackgroundMethod
- Authors
- Berg, H., & Hjelmervik, K. T.
- Affiliations
- Norwegian Defence Research Establishment (NO)
- Venue
- OCEANS 2018 MTS/IEEE Charleston
- Publisher
- IEEE
- Published
- 2018-10
- OpenAlex
- W2909509336
- Cited by
- 22
- References
- 1
- Citations by year
- 2024: 3 · 2023: 3 · 2022: 7 · 2021: 3 · 2020: 3 · 2019: 3
- FWCI
- 14.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- MTS/IEEE OCEANS Conference (OCEANS) (publishedVersion)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
- Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- sonar target classification, false alarm reduction, deep neural network, data augmentation, littoral zone, autonomous underwater vehicles
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