Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/60049
Title: Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN
Authors: Parras, Juan
Zazo, Santiago
Pérez Álvarez, Iván Alejandro 
Sanz Gonzalez, Jose Luis
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Wireless networks
Noise levels
Acoustic noise
Propagation
Underwater
Issue Date: 2019
Journal: Sensors 
Abstract: 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.
URI: http://hdl.handle.net/10553/60049
ISSN: 1424-8220
DOI: 10.3390/s19163530
Source: Sensors [ISSN 1424-8220], v. 19(16), 3530
Appears in Collections:Artículos
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