{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:03:04Z","timestamp":1771466584496,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2014Brasil (CAPES)","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The noise radiated from ships can be used for their identification and classification using passive sonar systems. Several techniques have been proposed for military ship classification based on acoustic signatures, which can be acquired through controlled experiments performed in an acoustic lane. The cost for such data acquisition is a significant issue since the ship and crew have to be dislocated from the fleet. In addition, the experiments have to be repeated for different operational conditions, taking a considerable amount of time. Even with this massive effort, the scarce amount of data produced by these controlled experiments may limit further detailed analyses. In this paper, deep learning models are used for full exploitation of such acquired data, envisaging passive sonar signal classification. A drawback of such models is the large number of parameters, which requires extensive data volumes for parameter tuning along the training phase. Thus, generative adversarial networks (GANs) are used to synthesize data so that a larger data volume can be produced for training convolutional neural networks (CNNs), which are used for the classification task. Different GAN design approaches were evaluated and both maximum probability and class-expert strategies were exploited for signal classification. Special attention was paid to how the expert knowledge might give a handle on analyzing the performance of the various deep learning models through tests that mirrored actual deployment. An accuracy as high as 99.0\u00b10.4% was achieved using experimental data, which improves upon previous machine learning designs in the field.<\/jats:p>","DOI":"10.3390\/rs14112648","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T21:43:42Z","timestamp":1654119822000},"page":"2648","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Deep Learning Models for Passive Sonar Signal Classification of Military Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5472-3544","authenticated-orcid":false,"given":"J\u00falio de Castro Vargas","family":"Fernandes","sequence":"first","affiliation":[{"name":"Signal Processing Lab, COPPE\/POLI, Technology Center, Federal University of Rio de Janeiro (UFRJ), Av. Hor\u00e1cio Macedo 2030, Rio de Janeiro 21941-914, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0828-6085","authenticated-orcid":false,"given":"Natanael Nunes","family":"de Moura Junior","sequence":"additional","affiliation":[{"name":"Signal Processing Lab, COPPE\/POLI, Technology Center, Federal University of Rio de Janeiro (UFRJ), Av. Hor\u00e1cio Macedo 2030, Rio de Janeiro 21941-914, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5148-7363","authenticated-orcid":false,"given":"Jos\u00e9 Manoel","family":"de Seixas","sequence":"additional","affiliation":[{"name":"Signal Processing Lab, COPPE\/POLI, Technology Center, Federal University of Rio de Janeiro (UFRJ), Av. Hor\u00e1cio Macedo 2030, Rio de Janeiro 21941-914, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Creasey, D.J. (1976). Sonar Methods. Remote Sensing for Environmental Sciences, Springer.","DOI":"10.1007\/978-3-642-66236-2_8"},{"key":"ref_2","unstructured":"Burdic, W.S. (1984). Underwater Acoustic System Analysis, Prentice-Hall."},{"key":"ref_3","unstructured":"(2022, May 12). Underwater Noise. Available online: https:\/\/www.ospar.org\/work-areas\/eiha\/noise."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jensen, F.B., Kuperman, W.A., Porter, M.B., and Schmidt, H. (2011). Computational Ocean Acoustics, Springer Science & Business Media.","DOI":"10.1007\/978-1-4419-8678-8"},{"key":"ref_5","unstructured":"Urick, R. (1986). Ambient Noise in the Sea, Peninsula Publishing."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.apacoust.2011.03.003","article-title":"Shallow ambient noise variability due to distant shipping noise and tide","volume":"72","author":"Das","year":"2011","journal-title":"Appl. Acoust."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Q. (2012). Digital Sonar Design in Underwater Acoustics: Principles and Applications, Springer. Available online: https:\/\/link.springer.com\/book\/10.1007\/978-3-642-18290-7.","DOI":"10.1007\/978-3-642-18290-7"},{"key":"ref_8","unstructured":"(2022, May 12). Non-Acoustic Submarine Detection\u2014A Technology Primer. Available online: https:\/\/res.cloudinary.com\/csisideaslab\/image\/upload\/v1574455202\/on-the-radar\/Non-acoustic_Sub_Detection_Primer_c7ntof.pdf."},{"key":"ref_9","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http:\/\/www.deeplearningbook.org."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Taha, T., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M., Hasan, M., Essen, B., Awwal, A., and Asari, V. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8.","DOI":"10.3390\/electronics8030292"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3058","DOI":"10.1109\/JSEN.2018.2886368","article-title":"Convolutional neural network with second-order pooling for underwater target classification","volume":"19","author":"Cao","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_13","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Ghahramani","year":"2014","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1049\/iet-rsn.2015.0179","article-title":"Class-modular multi-layer perceptron networks for supporting passive sonar signal classification","volume":"10","year":"2016","journal-title":"IET Radar Sonar Navig."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.epsr.2006.12.011","article-title":"Detection and classification of power quality disturbances using S-transform and modular neural network","volume":"78","author":"Bhende","year":"2008","journal-title":"Electr. Power Syst. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"961257","DOI":"10.1155\/2012\/961257","article-title":"Review Article Multivoxel Pattern Analysis for fMRI Data: A Review","volume":"2012","author":"Mahmoudi","year":"2012","journal-title":"Comput. Math. Methods Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","article-title":"Cross-Validatory Choice and Assessment of Statistical Predictions","volume":"36","author":"Stone","year":"1974","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_18","unstructured":"Nielsen, R.O. (1991). Sonar Signal Processing, Artech House, Inc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1049\/iet-rsn.2010.0157","article-title":"Preprocessing passive sonar signals for neural classification","volume":"5","year":"2011","journal-title":"IET Radar Sonar Navig."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cao, X., Zhang, X., Yu, Y., and Niu, L. (2016, January 16\u201318). Deep learning-based recognition of underwater target. Proceedings of the 2016 IEEE International Conference on Digital Signal Processing (DSP), Beijing, China.","DOI":"10.1109\/ICDSP.2016.7868522"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.apacoust.2010.09.006","article-title":"Acoustic detection and classification of river boats","volume":"72","author":"Averbuch","year":"2011","journal-title":"Appl. Acoust."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1109\/JOE.2002.1040944","article-title":"A study of effects of sonar bandwidth for underwater target classification","volume":"27","author":"Yao","year":"2002","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/72.846748","article-title":"Underwater target classification using wavelet packets and neural networks","volume":"11","author":"Yao","year":"2000","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2242","DOI":"10.1121\/1.4920186","article-title":"A wave structure based method for recognition of marine acoustic target signals","volume":"137","author":"Meng","year":"2015","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2265","DOI":"10.1121\/1.4900181","article-title":"The classification of underwater acoustic target signals based on wave structure and support vector machine","volume":"136","author":"Meng","year":"2014","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jiang, X., Wang, Q., and Zeng, X. (2013, January 2\u201313). Cavitation noise classification based on spectral statistic features and PCA algorithm. Proceedings of the 2013 3rd International Conference on Computer Science and Network Technology, Dalian, China.","DOI":"10.1109\/ICCSNT.2013.6967148"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/S0895-7177(97)00259-8","article-title":"Classification of underwater signals using wavelet transforms and neural networks","volume":"27","year":"1998","journal-title":"Math. Comput. Model."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.apacoust.2013.11.003","article-title":"Robust underwater noise targets classification using auditory inspired time\u2013frequency analysis","volume":"78","author":"Wang","year":"2014","journal-title":"Appl. Acoust."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Park, J., and Jung, D. (2019, January 15\u201318). Identifying Tonal Frequencies in a Lofargram with Convolutional Neural Networks. Proceedings of the 2019 19th International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea.","DOI":"10.23919\/ICCAS47443.2019.8971701"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"dos Santos Mello, V., de Moura, N.N., and de Seixas, J.M. (2018, January 8\u201313). Novelty Detection in Passive Sonar Systems using Stacked AutoEncoders. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489559"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wu, M., Wang, Q., Rigall, E., Li, K., Zhu, W., He, B., and Yan, T. (2019). ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation. Sensors, 19.","DOI":"10.3390\/s19092009"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yue, H., Zhang, L., Wang, D., Wang, Y., and Lu, Z. (2017, January 25\u201326). The Classification of Underwater Acoustic Targets Based on Deep Learning Methods. Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017), Sanya, China.","DOI":"10.2991\/caai-17.2017.118"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/0004-3702(92)90065-6","article-title":"Connectionist Learning of Belief Networks","volume":"56","author":"Neal","year":"1992","journal-title":"Artif. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hong, F., Liu, C., Guo, L., Chen, F., and Feng, H. (2021). Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method. Appl. Sci., 11.","DOI":"10.3390\/app11041442"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, M., Yuan, F., Zhu, Y., and Cheng, E. (2018, January 28\u201331). Generating Underwater Images by GANs and Similarity Measurement. Proceedings of the 2018 OCEANS-MTS\/IEEE Kobe Techno-Oceans (OTO), Kobe, Japan.","DOI":"10.1109\/OCEANSKOBE.2018.8559298"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhang, Y., Wang, H., and Liu, X. (2017, January 22\u201325). Underwater image classification using deep convolutional neural networks and data augmentation. Proceedings of the 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xiamen, China.","DOI":"10.1109\/ICSPCC.2017.8242527"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sung, M., Kim, J., and Yu, S.C. (2018, January 28\u201331). Image-based Super Resolution of Underwater Sonar Images using Generative Adversarial Network. Proceedings of the TENCON 2018 IEEE Region 10 Conference, Jeju, Korea.","DOI":"10.1109\/TENCON.2018.8650176"},{"key":"ref_38","unstructured":"Rixon Fuchs, L., Larsson, C., and G\u00e4llstr\u00f6m, A. (July, January 30). Deep learning based technique for enhanced sonar imaging. Proceedings of the 5th Underwater Acoustics Conference and Exhibition, Hersonissos, Crete, Greece."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jegorova, M., Ilari Karjalainen, A., Vazquez, J., and Hospedales, T. (2019). Full-Scale Continuous Synthetic Sonar Data Generation with Markov Conditional Generative Adversarial Networks. arXiv.","DOI":"10.1109\/ICRA40945.2020.9197353"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6014","DOI":"10.1109\/ACCESS.2016.2611492","article-title":"Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review","volume":"4","author":"Gill","year":"2016","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1080\/0952813X.2019.1647560","article-title":"Deep learning-based framework for expansion, recognition and classification of underwater acoustic signal","volume":"32","author":"Jin","year":"2020","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"012104","DOI":"10.1088\/1742-6596\/1544\/1\/012104","article-title":"GAN-based Sample Expansion for Underwater Acoustic Signal","volume":"1544","author":"Yang","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_43","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P. (2016, January 5\u201310). InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. Proceedings of the Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.apacoust.2016.06.008","article-title":"ShipsEar: An underwater vessel noise database","volume":"113","year":"2016","journal-title":"Appl. Acoust."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, F., Song, Q., and Jin, G. (2018, January 12\u201314). Expansion of restricted sample for underwater acoustic signal based on generative adversarial networks. Proceedings of the Tenth International Conference on Graphics and Image Processing (ICGIP 2018), Chengdu, China.","DOI":"10.1117\/12.2524173"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chavdarova, T., and Fleuret, F. (2017, January 18\u201323). SGAN: An Alternative Training of Generative Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00980"},{"key":"ref_47","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein Generative Adversarial Networks. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_48","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A.C. (2017, January 4\u20139). Improved Training of Wasserstein GANs. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_49","unstructured":"(2014). Optimal Transport: Theory and Applications, Cambridge University Press. London Mathematical Society Lecture Note Series."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1287\/mnsc.6.4.366","article-title":"Mathematical Methods of Organizing and Planning Production","volume":"6","author":"Kantorovich","year":"1960","journal-title":"Manag. Sci."},{"key":"ref_51","unstructured":"Evans, L.C., and Gariepy, R.F. (1992). Measure Theory and Fine Properties of Functions, Studies in Advanced Mathematics; CRC Press."},{"key":"ref_52","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional Generative Adversarial Nets. arXiv."},{"key":"ref_53","first-page":"2642","article-title":"Conditional Image Synthesis With Auxiliary Classifier GANs","volume":"70","author":"Odena","year":"2017","journal-title":"PMLR"},{"key":"ref_54","unstructured":"Lin, Z., Khetan, A., Fanti, G., and Oh, S. (2017). PacGAN: The power of two samples in generative adversarial networks. arXiv."},{"key":"ref_55","unstructured":"Regazonni, C., Tesei, A., and Tacconi, G. (1994, January 19\u201322). A comparison between spectral and bispectral analysis for ship detection from acoustical time series. Proceedings of the ICASSP \u201994, IEEE International Conference on Acoustics, Speech and Signal Processing, Adelaide, Australia."},{"key":"ref_56","unstructured":"Pflug, L.A., Ioup, G.E., Ioup, J.W., and Jackson, P. (1997, January 21\u201323). Variability in higher order statistics of measured shallow-water shipping noise. Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, Banff, AB, Canada."},{"key":"ref_57","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. arXiv.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_59","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_60","unstructured":"Japkowicz, N., and Shah, M. (2014). Evaluating Learning Algorithms: A Classification Perspective, Cambridge University Press."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Friedman, J., Hastie, T., and Tibshirani, R. (2001). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On information and sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.nima.2005.11.132","article-title":"Neural triggering system operating on high resolution calorimetry information","volume":"559","author":"Torres","year":"2006","journal-title":"Nucl. Instruments Methods Phys. Res. Sect. Accel. Spectrometers Detect. Assoc. Equip."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Hodges, R.P. (2011). Underwater Acoustics: Analysis, Design and Performance of Sonar, John Wiley & Sons.","DOI":"10.1002\/9780470665244"},{"key":"ref_65","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2648\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:23:25Z","timestamp":1760138605000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2648"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,1]]},"references-count":65,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112648"],"URL":"https:\/\/doi.org\/10.3390\/rs14112648","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,1]]}}}