{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T04:26:59Z","timestamp":1769315219043,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62201167"],"award-info":[{"award-number":["62201167"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Shallow water passive source localization is an essential problem in underwater detection and localization. Traditional matched-field processing (MFP) methods are sensitive to environment mismatches. Many neural network localization methods still have room for improvement in accuracy if they are further adjusted to underwater acoustic characteristics. To address these problems, we propose a deep learning localization method via improved input features and network structure, which can effectively estimate the depth and the closest point of approach (CPA) range of the acoustic source. Firstly, we put forward a feature preprocessing scheme to enhance the localization accuracy and robustness. Secondly, we design a deep learning network structure to improve the localization accuracy further. Finally, we propose a method of visualizing the network to optimize the estimated localization results. Simulations show that the accuracy of the proposed method is better than other compared features and network structures, and the robustness is significantly better than that of the MFP methods. Experimental results further prove the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/rs16081391","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T03:56:13Z","timestamp":1713153373000},"page":"1391","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Deep Learning Localization Method for Acoustic Source via Improved Input Features and Network Structure"],"prefix":"10.3390","volume":"16","author":[{"given":"Dajun","family":"Sun","sequence":"first","affiliation":[{"name":"National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Marine Information Acquisition and Security, Harbin Engineering University, Ministry of Industry and Information Technology, Harbin 150001, China"},{"name":"College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9269-5755","authenticated-orcid":false,"given":"Xiaoying","family":"Fu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Marine Information Acquisition and Security, Harbin Engineering University, Ministry of Industry and Information Technology, Harbin 150001, China"},{"name":"College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingting","family":"Teng","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Marine Information Acquisition and Security, Harbin Engineering University, Ministry of Industry and Information Technology, Harbin 150001, China"},{"name":"College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3412","DOI":"10.1109\/TVT.2020.2968500","article-title":"Peak Extraction Passive Source Localization Using a Single Hydrophone in Shallow Water","volume":"69","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3090","DOI":"10.1109\/TSP.2022.3173731","article-title":"A Semi-Blind Method for Localization of Underwater Acoustic Sources","volume":"70","author":"Weiss","year":"2022","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5825","DOI":"10.1109\/TSP.2014.2360818","article-title":"Matched-Field Processing Performance Under the Stochastic and Deterministic Signal Models","volume":"62","author":"Socheleau","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3628","DOI":"10.1121\/1.5040492","article-title":"Stochastic matched-field localization of an acoustic source based on principles of Riemannian geometry","volume":"143","author":"Finette","year":"2018","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2777","DOI":"10.1121\/1.402958","article-title":"Broadband matched-field source localization","volume":"91","author":"Westwood","year":"1992","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1109\/JOE.2004.835788","article-title":"Broad-band matched-field source localization in the east China Sea","volume":"29","author":"Zhang","year":"2004","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4812","DOI":"10.1121\/1.5138134","article-title":"Bayesian coherent and incoherent matched-field localization and detection in the ocean","volume":"146","author":"Michalopoulou","year":"2019","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1109\/JOE.2019.2927652","article-title":"Matched Field Processing in Phase Space","volume":"45","author":"Virovlyansky","year":"2020","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2563","DOI":"10.1121\/1.428644","article-title":"The matched-phase coherent multi-frequency matched-field processor","volume":"107","author":"Orris","year":"2000","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/JOE.2011.2181269","article-title":"Source Localization Using Matched-Phase Matched-Field Processing With Phase Descent Search","volume":"37","author":"Chen","year":"2012","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1218","DOI":"10.1121\/1.4863270","article-title":"Data-based matched-mode source localization for a moving source","volume":"135","author":"Yang","year":"2014","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1121\/1.4976089","article-title":"Stable components of sound fields in the ocean","volume":"141","author":"Virovlyansky","year":"2017","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/48.211502","article-title":"Fast matched field processing","volume":"18","author":"Aravindan","year":"1993","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sun, Z., Meng, C., Cheng, J., Zhang, Z., and Chang, S. (2022). A multi-scale feature pyramid network for detection and instance segmentation of marine ships in SAR images. Remote Sens., 14.","DOI":"10.3390\/rs14246312"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2024.3480206","article-title":"IFAN: An Icosahedral Feature Attention Network for Sound Source Localization","volume":"73","author":"Zhu","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, Y., Si, Y., Tong, Z., He, L., Zhang, J., Luo, S., and Gong, Y. (2022). MQANet: Multi-Task Quadruple Attention Network of Multi-Object Semantic Segmentation from Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14246256"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3590","DOI":"10.1121\/1.5133944","article-title":"Machine learning in acoustics: Theory and applications","volume":"146","author":"Bianco","year":"2019","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3204","DOI":"10.1121\/10.0006783","article-title":"Introduction to the special issue on machine learning in acoustics","volume":"150","author":"Michalopoulou","year":"2021","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/JOE.2023.3290987","article-title":"Underwater Multitarget Tracking Method Based on Threshold Segmentation","volume":"48","author":"Zhou","year":"2023","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1121\/1.5032311","article-title":"Underwater acoustic source localization using generalized regression neural network","volume":"143","author":"Wang","year":"2018","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1121\/1.5000165","article-title":"Source localization in an ocean waveguide using supervised machine learning","volume":"142","author":"Niu","year":"2017","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, X., Dong, H., Salvo Rossi, P., and Landr\u00f8, M. (2021). Feature selection based on principal component regression for underwater source localization by deep learning. Remote Sens., 13.","DOI":"10.3390\/rs13081486"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1121\/10.0003361","article-title":"Learning location and seabed type from a moving mid-frequency source","volume":"149","author":"Neilsen","year":"2021","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3426","DOI":"10.1121\/10.0011470","article-title":"Robust high-resolution direction-of-arrival estimation method using DenseBlock-based U-net","volume":"151","author":"Sun","year":"2022","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_26","first-page":"4204912","article-title":"High-rate underwater acoustic localization based on the decision tree","volume":"60","author":"Sun","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1121\/1.5116016","article-title":"Deep-learning source localization using multi-frequency magnitude-only data","volume":"146","author":"Niu","year":"2019","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2922","DOI":"10.1121\/1.5036725","article-title":"Source localization using deep neural networks in a shallow water environment","volume":"143","author":"Huang","year":"2018","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109530","DOI":"10.1016\/j.apacoust.2023.109530","article-title":"A feature-compressed multi-task learning U-Net for shallow-water source localization in the presence of internal waves","volume":"211","author":"Qian","year":"2023","journal-title":"Appl. Acoust."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"EL317","DOI":"10.1121\/1.5126923","article-title":"Deep transfer learning for source ranging: Deep-sea experiment results","volume":"146","author":"Wang","year":"2019","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Agrawal, S., and Sharma, D.K. (2022, January 23\u201325). Feature extraction and selection techniques for time series data classification: A comparative analysis. Proceedings of the 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India.","DOI":"10.23919\/INDIACom54597.2022.9763125"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2280","DOI":"10.1121\/1.400918","article-title":"A posteriori probability source localization in an uncertain sound speed, deep ocean environment","volume":"89","author":"Richardson","year":"1991","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Schaeffer-Filho, A., Smith, P., Mauthe, A., Hutchison, D., Yu, Y., and Fry, M. (2012, January 16\u201320). A framework for the design and evaluation of network resilience management. Proceedings of the 2012 IEEE Network Operations and Management Symposium, Maui, HI, USA.","DOI":"10.1109\/NOMS.2012.6211924"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/TNSRE.2016.2601240","article-title":"A deep learning scheme for motor imagery classification based on restricted Boltzmann machines","volume":"25","author":"Lu","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Saha, A., Rathore, S.S., Sharma, S., and Samanta, D. (2019, January 7\u20139). Analyzing the difference between deep learning and machine learning features of EEG signal using clustering techniques. Proceedings of the 2019 IEEE Region 10 Symposium (TENSYMP), Kolkata, India.","DOI":"10.1109\/TENSYMP46218.2019.8971358"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1121\/10.0004597","article-title":"Seabed type and source parameters predictions using ship spectrograms in convolutional neural networks","volume":"149","author":"Neilsen","year":"2021","journal-title":"J. Acoust. Soc. Am."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1391\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:27:57Z","timestamp":1760106477000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1391"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,14]]},"references-count":41,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16081391"],"URL":"https:\/\/doi.org\/10.3390\/rs16081391","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,14]]}}}