{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:48:14Z","timestamp":1770842894089,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T00:00:00Z","timestamp":1565654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["TEC2016-76038-C3-1-R (HERAKLES)"],"award-info":[{"award-number":["TEC2016-76038-C3-1-R (HERAKLES)"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, there has been a significant effort towards developing localization systems in the underwater medium, with current methods relying on anchor nodes, explicitly modeling the underwater channel or cooperation from the target. Lately, there has also been some work on using the approximation capabilities of Deep Neural Networks in order to address this problem. In this work, we study how the localization precision of using Deep Neural Networks is affected by the variability of the channel, the noise level at the receiver, the number of neurons of the neural network and the utilization of the power or the covariance of the received acoustic signals. Our study shows that using deep neural networks is a valid approach when the channel variability is low, which opens the door to further research in such localization methods for the underwater environment.<\/jats:p>","DOI":"10.3390\/s19163530","type":"journal-article","created":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T04:31:21Z","timestamp":1565670681000},"page":"3530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7028-3179","authenticated-orcid":false,"given":"Juan","family":"Parras","sequence":"first","affiliation":[{"name":"Information Processing and Telecommunications Center, Universidad Polit\u00e9cnica de Madrid, ETSI Telecomunicaci\u00f3n, Av. Complutense 30, 28040 Madrid, Spain"}]},{"given":"Santiago","family":"Zazo","sequence":"additional","affiliation":[{"name":"Information Processing and Telecommunications Center, Universidad Polit\u00e9cnica de Madrid, ETSI Telecomunicaci\u00f3n, Av. Complutense 30, 28040 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5990-8409","authenticated-orcid":false,"given":"Iv\u00e1n A.","family":"P\u00e9rez-\u00c1lvarez","sequence":"additional","affiliation":[{"name":"Institute for Technological Development and Innovation in Communications (IDeTIC), Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas, Spain"}]},{"given":"Jos\u00e9 Luis","family":"Sanz Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Information Processing and Telecommunications Center, Universidad Polit\u00e9cnica de Madrid, ETSI Telecomunicaci\u00f3n, Av. Complutense 30, 28040 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"896832","DOI":"10.1155\/2015\/896832","article-title":"Underwater sensor network applications: A comprehensive survey","volume":"11","author":"Felemban","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/SURV.2011.020211.00035","article-title":"A survey of architectures and localization techniques for underwater acoustic sensor networks","volume":"13","author":"Mouftah","year":"2011","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1016\/j.oceaneng.2011.07.017","article-title":"A survey of techniques and challenges in underwater localization","volume":"38","author":"Tan","year":"2011","journal-title":"Ocean Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2026","DOI":"10.3390\/s120202026","article-title":"Localization algorithms of underwater wireless sensor networks: A survey","volume":"12","author":"Han","year":"2012","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chandrasekhar, V., Seah, W.K., Choo, Y.S., and Ee, H.V. (2006, January 25). Localization in underwater sensor networks: Survey and challenges. Proceedings of the 1st ACM International Workshop on Underwater Networks, Los Angeles, CA, USA.","DOI":"10.1145\/1161039.1161047"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Diamant, R., Tan, H.P., and Lampe, L. (2010, January 20\u201323). NLOS identification using a hybrid ToA-signal strength algorithm for underwater acoustic localization. Proceedings of the OCEANS 2010 MTS\/IEEE SEATTLE, Seattle, WA, USA.","DOI":"10.1109\/OCEANS.2010.5664483"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1109\/TMC.2010.158","article-title":"Scalable localization with mobility prediction for underwater sensor networks","volume":"10","author":"Zhou","year":"2011","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zazo, J., Macua, S.V., Zazo, S., P\u00e9rez, M., P\u00e9rez-\u00c1lvarez, I., Jim\u00e9nez, E., Cardona, L., Brito, J.H., and Quevedo, E. (2016). Underwater electromagnetic sensor networks, part II: Localization and network simulations. Sensors, 16.","DOI":"10.3390\/s16122176"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dubrovinskaya, E., Diamant, R., and Casari, P. (2017, January 25\u201326). Anchorless underwater acoustic localization. Proceedings of the 2017 14th Workshop on Positioning, Navigation and Communications (WPNC), Bremen, Germany.","DOI":"10.1109\/WPNC.2017.8250051"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1049\/iet-com.2015.0469","article-title":"Multi-layer neural network for received signal strength-based indoor localisation","volume":"10","author":"Dai","year":"2016","journal-title":"IET Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1007\/s11277-015-2362-x","article-title":"Analysis of some feedforward artificial neural network training algorithms for developing localization framework in wireless sensor networks","volume":"82","author":"Payal","year":"2015","journal-title":"Wirel. Pers. Commun."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Huang, Z., Xu, J., Gong, Z., Wang, H., and Yan, Y. (2018, January 15\u201320). A Deep Neural Network Based Method of Source Localization in a Shallow Water Environment. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8461860"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1109\/JOE.2013.2278787","article-title":"Statistical characterization and computationally efficient modeling of a class of underwater acoustic communication channels","volume":"38","author":"Qarabaqi","year":"2013","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ondruska, P., and Posner, I. (2016, January 2). Deep tracking: Seeing beyond seeing using recurrent neural networks. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AR, USA.","DOI":"10.1609\/aaai.v30i1.10413"},{"key":"ref_16","unstructured":"Qarabaqi, P., and Stojanovic, M. (November, January 31). Statistical modeling of a shallow water acoustic communication channel. Proceedings of the 2010\u2014MILCOM 2010 Military Communications Conference, San Jose, CA, USA."},{"key":"ref_17","unstructured":"Socheleau, F.X., Passerieux, J.M., and Laot, C. (2009, January 21\u201326). Characterisation of time-varying underwater acoustic communication channel with application to channel capacity. Proceedings of the Underwater Acoustic Measurements, Nafplion, Greece."},{"key":"ref_18","unstructured":"Tomasi, B., Casari, P., Badia, L., and Zorzi, M. (October, January 30). A study of incremental redundancy hybrid ARQ over Markov channel models derived from experimental data. Proceedings of the Fifth ACM International Workshop on UnderWater Networks, Woods Hole, MA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1121\/1.2346133","article-title":"High-frequency channel characterization for M-ary frequency-shift-keying underwater acoustic communications","volume":"120","author":"Yang","year":"2006","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_20","unstructured":"Brekhovskikh, L., Lysanov, J.P., and Lysanov, Y.P. (2003). Fundamentals of Ocean Acoustics, Springer Science & Business Media."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Etter, P.C. (2018). Underwater Acoustic Modeling and Simulation, CRC Press.","DOI":"10.1201\/9781315166346"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s11633-017-1053-3","article-title":"A survey on deep learning-based fine-grained object classification and semantic segmentation","volume":"14","author":"Zhao","year":"2017","journal-title":"Int. J. Autom. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_26","unstructured":"Wang, X., Gao, L., Mao, S., and Pandey, S. (2015, January 9\u201312). DeepFi: Deep learning for indoor fingerprinting using channel state information. Proceedings of the Wireless Communications and Networking Conference (WCNC), New Orleans, LA, USA."},{"key":"ref_27","unstructured":"Hecht-Nielsen, R. (1992). Theory of the backpropagation neural network. Neural Networks for Perception, Elsevier."},{"key":"ref_28","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/5.58337","article-title":"Backpropagation through time: What it does and how to do it","volume":"78","author":"Werbos","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ostrowski, Z., Marszal, J., and Salamon, R. (2018, January 11\u201314). Underwater Navigation System Based on Doppler Shifts of a Continuous Wave. Proceedings of the 2018 Joint Conference-Acoustics, Ustka, Poland.","DOI":"10.1109\/ACOUSTICS.2018.8502410"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.oceaneng.2015.12.040","article-title":"Propeller cavitation noise investigations of a research vessel using medium size cavitation tunnel tests and full-scale trials","volume":"120","author":"Aktas","year":"2016","journal-title":"Ocean Eng."},{"key":"ref_33","unstructured":"Nielsen, R.O. (1991). Sonar Signal Processing, Artech House, Inc."},{"key":"ref_34","first-page":"28","article-title":"Simulation and experimentation platforms for underwater acoustic sensor networks: Advancements and challenges","volume":"50","author":"Luo","year":"2017","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_35","unstructured":"(2019, July 10). Acoustic Channel Modeling and Simulation. Available online: http:\/\/millitsa.coe.neu.edu\/?q=projects."},{"key":"ref_36","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/JOE.2012.2195852","article-title":"Detection of surface ships from interception of cyclostationary signature with the cyclic modulation coherence","volume":"37","author":"Antoni","year":"2012","journal-title":"IEEE J. Ocean. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/16\/3530\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:10:38Z","timestamp":1760188238000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/16\/3530"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,13]]},"references-count":37,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["s19163530"],"URL":"https:\/\/doi.org\/10.3390\/s19163530","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,13]]}}}