An automated approach to passive sonar classification using binary image features
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The acoustic signal produced by a vessel moving in the sea is mainly composed of a broadband component generated by the propeller and its hydrodynamic interactions, and a narrow band component due to the propulsion system and other mechanical parts.
“The acoustic signal produced by a vessel moving in the sea is mainly composed of a broadband component (with a continuous spectrum), that is generated by the propeller and its hydrodynamic interactions; and a narrow band component (whose spectrum consists of line components at discrete frequencies), owing to the propulsion system and other mechanical parts [19]”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [19]
An experiment with three different SNR (without noise; 5 dB and 10 dB) compared the accuracy of the proposed model with a fractal-based method, showing that NN have better noise robustness for all analysed scenarios.
“An experiment with three different SNR (without noise; 5 dB and 10 dB) compared the accuracy of the proposed model with a fractal-based method, showing that NN have a better noise robustness for all analysed scenarios.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [19]
Results indicate that for the five-class classification problem, the feature extraction method proposed in [19] achieved high-performance recognition with just 11 features.
“Results also indicate that for the five-class classification problem, the feature extraction method proposed achieved a high-performance recognition with just 11 features.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [19]
Cites
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,525 · see citing works
Discrete-Time Signal Processing
Oppenheim, A. V., & Schafer, R. W. (1989) · Cited by 11,808 · see citing works
Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000) · Advances in industrial control · doi:10.1007/978-1-4471-0409-4_3 · Cited by 7,938 · see citing works
Principles of underwater sound
Urick, R. J. (1975) · Virtual Defense Library (Ministerio de Defensa) · Cited by 3,045 · see citing works
Wang, Y., Li, J., & Stoica, P. (2005) · Synthesis lectures on signal processing · doi:10.1007/978-3-031-02525-9 · Cited by 2,887 · see citing works
Duda, R. O., Hart, P. E. 1., & Stork, D. G. C. 2. /. J. (2001) · Cited by 2,469 · see citing works
Principles of underwater sound
Zimmer, W. M. X. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511977107.003 · Cited by 2,258 · see citing works
Proceedings of 2004 IEEE International Symposium on Circuits and Systems
(2004) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2004.1328657 · Cited by 804 · see citing works
Optimum signal processing : an introduction
Orfanidis, S. J. (1988) · Macmillan , Collier-Macmillan eBooks · Cited by 337 · see citing works
Zue, V. W., Cole, R., & Ward, W. (1997) · Cited by 288 · see citing works
Optimum signal processing: An introduction
Wakefield, G. H. (1986) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1986.1164870 · Cited by 204 · see citing works
Underwater Localization with Time-Synchronization and Propagation Speed Uncertainties
Diamant, R., & Lampe, L. (2012) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2012.100 · Cited by 142 · see citing works
Passive acoustic detection of deep-diving beaked whales
Zimmer, W. M. X., Harwood, J., Tyack, P. L., Johnson, M. P., & Madsen, P. T. (2008) · The Journal of the Acoustical Society of America · doi:10.1121/1.2988277 · Cited by 130 · see citing works
WSEAS TRANSACTIONS ON SIGNAL PROCESSING
(2020) · WSEAS Transactions on Signal Processing archive · doi:10.37394/232014 · Cited by 124 · see citing works
Ship Detection with Wireless Sensor Networks
Luo, H., Wu, K., Guo, Z., Gu, L., & Ni, L. M.-S. (2011) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2011.274 · Cited by 71 · see citing works
Proceedings of 2018 International Conference on Wavelet Analysis and Pattern Recognition
(2018) · International Conference on Wavelet Analysis and Pattern Recognition · doi:10.1109/icwapr.2018.8521322 · Cited by 66 · see citing works
Ship recognition via its radiated sound: The fractal based approaches
Yang, S., Zhi-shun, L., & Wang, X. (2002) · The Journal of the Acoustical Society of America · doi:10.1121/1.1487840 · Cited by 48 · see citing works
Bao, F., Li, C., Wang, X., Wang, Q., & Du, S. (2010) · The Journal of the Acoustical Society of America · doi:10.1121/1.3436543 · Cited by 47 · see citing works
Classification of Ships Using Underwater Radiated Noise
Lourens, J. G. (1989) · Springer eBooks · doi:10.1007/978-94-009-2289-1_66 · Cited by 36 · see citing works
Target classification in a passive sonar-an expert system approach
Rajagopal, R., Sankaranarayanan, B., & Rao, P. R. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1990.116235 · Cited by 34 · see citing works
Principal component analysis for classifying passive sonar signals
Soares-Filho, W., de Seixas, J. M., & Calôba, L. P. (2002) · doi:10.1109/iscas.2001.921380 · Cited by 31 · see citing works
Passive polarimetric IR target classification
Sadjadi, F., & Chun, C. S. L. (2001) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/7.937487 · Cited by 28 · see citing works
Proceedings 1995 Canadian Conference on Electrical and Computer Engineering
(1995) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.1995.528060 · Cited by 23 · see citing works
Passive sonar recognition and analysis using hybrid neural networks
Howell, B., & Wood, S. L. (2003) · Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492) · doi:10.1109/oceans.2003.178182 · Cited by 22 · see citing works
Ship noise classification using Probabilistic Neural Network and AR model coefficients
Farrokhrooz, M., & Karimi, M. (2005) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceanse.2005.1513213 · Cited by 22 · see citing works
Sonar signal detection and classification using artificial neural networks
Ward, M. K., & Stevenson, M. (2002) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2000.849558 · Cited by 19 · see citing works
Eickstedt, D. P., & Schmidt, H. (2003) · Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492) · doi:10.1109/oceans.2003.178074 · Cited by 19 · see citing works
Classification of ship-radiated signals via chaotic features
Yang, S., & Zhi-shun, L. (2003) · Electronics Letters · doi:10.1049/el:20030258 · Cited by 18 · see citing works
Classification of ships using underwater radiated noise
Lourens, J. G. (2003) · South African Symposium on Communications and Signal Processing (COMSIG) · doi:10.1109/comsig.1988.49315 · Cited by 17 · see citing works
An application of expert system in recognition of radiated noise of underwater target
Li, Q., Wang, J., & Wei, W. (2002) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans.1995.526801 · Cited by 16 · see citing works
Ship noise demodulation line spectrum fusion feature extraction based on the wavelet packet
Shi, G.-Z., & Hu, J. (2007) · International Conference on Wavelet Analysis and Pattern Recognition · doi:10.1109/icwapr.2007.4420787 · Cited by 14 · see citing works
Ships classification basing on acoustic signatures
Żak, A. (2008) · WSEAS Transactions on Signal Processing archive · Cited by 13 · see citing works
Farrokhrooz, M., & Karimi, M. (2011) · Intelligent Automation & Soft Computing · doi:10.1080/10798587.2011.10643155 · Cited by 12 · see citing works
Application of BP neural network and higher order spectrum for ship-radiated noise classification
Xi-ying, H., Jin-fang, C., Guang-jin, H., & Li, N. (2010) · International Conference on Future Computer and Communication · doi:10.1109/icfcc.2010.5497336 · Cited by 12 · see citing works
Fused Classification of Surface Ships Based on Hydroacoustic and Electromagnetic Signatures
Lennartsson, R. K., Dalberg, E., Levonen, M. J., Lindgren, D., & Persson, L. (2006) · OCEANS 2006 - Asia Pacific · doi:10.1109/oceansap.2006.4393910 · Cited by 11 · see citing works
Averaging spectra to improve the classification of the noise radiated by ships using neural networks
Soares-Filho, W., Seixas, J. M., & Calôba, L. P. (2002) · Brazilian Symposium on Neural Networks (SBRN) · doi:10.1109/sbrn.2000.889731 · Cited by 9 · see citing works
Vessel radiated noise recognition with fractalfeatures
Su, Y., Zhi-shun, L., & Wang, X. (2000) · Electronics Letters · doi:10.1049/el:20000651 · Cited by 7 · 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
“A fully connected neural network was applied to recognise ships in hydrophone data [19].”
BackgroundMethodInfluentialUnderwater acoustic target recognition based on spectrum component analysis of ship radiated noise
Zhu, P., Zhang, Y., Huang, Y., Zhao, C., Zhao, K., & Zhou, F. (2023) · Applied Acoustics · doi:10.1016/j.apacoust.2023.109552 · Cited by 78 · see citing works
Yang, S., Jin, A., Zeng, X., Wang, H., Hong, X., & Lei, M. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107983 · Cited by 55 · see citing works
Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · IEEE Access · doi:10.1109/access.2022.3220265 · Cited by 37 · see citing works
Underwater noise recognition of marine vessels passages: two case studies using hidden Markov models
Vieira, M., Amorim, M. C. P., Sundelöf, A., Prista, N., & Fonseca, P. J. (2019) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsz194 · Cited by 32 · see citing works
Zhu, P., Zhang, Y., Huang, Y., Lin, B., Zhu, M., Zhao, K., & Zhou, F. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3329653 · Cited by 21 · see citing works
Jin, A., Yang, S., Lei, M., Zeng, X., & Wang, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111791 · Cited by 17 · see citing works
Kubicek, B., Gupta, A. S., & Kirsteins, I. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11030571 · Cited by 7 · see citing works
Automated Road Extraction and Analysis from LiDAR Point Cloud Data Using Local Optimization
Liu, X., & Li, H. (2023) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-023-0919-x · Cited by 4 · see citing works
Zheng, Z., & Liu, P. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12111952 · Cited by 4 · see citing works
Li, W., Chen, Z., Wang, J., Liu, T., Chen, H., & Xu, G. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114252 · Cited by 2 · see citing works
Passive Sonar Classification Using Time-Domain Information and Recurrent Neural Networks
Souza, M. J., de Moura, N. N., & Seixas, J. M. (2022) · IEEE Latin American Conference on Computational Intelligence (LA-CCI) · doi:10.1109/la-cci54402.2022.9981792 · Cited by 2 · see citing works
Muye, Y., Lei, Z., Xiaofang, L., Wang, T., Chuanghui, Z., & Zanrong, S. (2025) · Advances in Mechanical Engineering · doi:10.1177/16878132251374885
- Authors
- Vahidpour, V., Rastegarnia, A., & Khalili, A.
- Affiliations
- Malayer University (IR)
- Corresponding author
- Rastegarnia, Amir
- Venue
- Journal of Marine Science and Application
- Published
- 2015-07-10
- Volume
- 14
- Issue
- 3
- Pages
- 327-333
- ISSN
- 1671-9433 (ISSN-L)
- 1993-5048
- OpenAlex
- W1914845144
- Cited by
- 15
- References
- 38
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 4 · 2022: 5 · 2019: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Marine Science and Application (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 › Signal Processing › Music and Audio Processing
- Keywords
- passive sonar, ship recognition, frequency spectrum, signal segmentation, noise reduction, neural network classifier, underwater acoustic signals
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