Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning
Open accessAbstract
Four data-driven methods—random forest (RF), support vector machine (SVM), feed-forward neural network (FNN), and convolutional neural network (CNN)—are applied to discriminate surface and underwater vessels in the ocean using low-frequency acoustic pressure data. Acoustic data are modeled considering a vertical line array by a Monte Carlo simulation using the underwater acoustic propagation model, KRAKEN, in the ocean environment of East Sea in Korea. The raw data are preprocessed and reorganized into the phone-space cross-spectral density matrix (pCSDM) and mode-space cross-spectral density matrix (mCSDM). Two additional matrices are generated using the absolute values of matrix elements in each CSDM. Each of these four matrices is used as input data for supervised machine learning. Binary classification is performed by using RF, SVM, FNN, and CNN, and the obtained results are compared. All machine-learning algorithms show an accuracy of >95% for three types of input data—the pCSDM, mCSDM, and mCSDM with the absolute matrix elements. The CNN is the best in terms of low percent error. In particular, the result using the complex pCSDM is encouraging because these data-driven methods inherently do not require environmental information. This work demonstrates the potential of machine learning to discriminate between surface and underwater vessels in the ocean.
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Results in [6] suggest that phone-spaced data inputs are more suitable to ML methods than mode space, as the former are independent from environmental information.
“Additionally, results suggest that phone-spaced data inputs are more suitable to ML methods than mode space, as the former are independent from environmental information.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [6]
The Discussion and Conclusions (section 5) says complex phone-space data performed comparably to complex mode-space data without requiring a priori ocean-environment information. But Classification Results (section 4.4) says absolute phone-space data performed worst, so the paper does not establish that phone-space inputs are generally more suitable for ML than mode-space inputs. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn an experiment using synthetic data from the KRAKEN normal-mode propagation model, the convolutional model produced the best accuracy results but showed high variability with respect to input data representation, whereas the feed-forward neural network was not affected.
“The convolutional model produced the best accuracy results; however, it also showed high variability with respect to the input data representation (where a phone-space representation presented as a more suitable input type, as discussed in Section 3), whereas the FNN was not affected.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [6]
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Experimental — accuracy not yet measuredAll algorithms in [6] presented excellent performance, with an error rate below 5%.
“All algorithms presented excellent performance, with an error rate below 5%.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [6]
The results reported in [6] confirm that the proposed machine-learning methods were robust with respect to SNR.
“The results reported confirm that the proposed machine-learning methods were robust with respect to SNR.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [6]
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- Authors
- Choi, J., Choo, Y., & Lee, K.
- Affiliations
- Sejong University (KR)
- Corresponding author
- Lee, Keunhwa
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-08-09
- Volume
- 19
- Issue
- 16
- Pages
- 3492
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6721123
- OpenAlex
- W2969080949
- Cited by
- 63
- References
- 24
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 14 · 2023: 8 · 2022: 13 · 2021: 10 · 2020: 3
- FWCI
- 3.59
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea (2019R1F1A1059146)
- Agency for Defense Development (UD160015DD)
- 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 › Signal Processing › Direction-of-Arrival Estimation Techniques
- Keywords
- surface ships, underwater vessels, cross-spectral density matrix, vertical line array, Monte Carlo simulation, convolutional neural networks, random forest, support vector machine, feedforward neural networks, Sea of Japan
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