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Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN

Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Av. Complutense 30, 28040 Madrid, Spain
Institute for Technological Development and Innovation in Communications (IDeTIC), Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas, Spain
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3530;
Received: 11 July 2019 / Revised: 6 August 2019 / Accepted: 10 August 2019 / Published: 13 August 2019
(This article belongs to the Section Sensor Networks)
PDF [1092 KB, uploaded 13 August 2019]


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. View Full-Text
Keywords: deep learning; underwater localization; acoustic deep learning; underwater localization; acoustic

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Parras, J.; Zazo, S.; Pérez-Álvarez, I.A.; Sanz González, J.L. Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN. Sensors 2019, 19, 3530.

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