Skip to Main content Skip to Navigation
Conference papers

Using deep learning for sonar targets localization

Q Bruel 1 F Heitzmann 2 D Morche 2 J Huillery 3 E Blanco 3 L Bako 3 
1 LIIM - Laboratoire Intelligence Intégrée Multi-capteurs
UGA - Université Grenoble Alpes, DSCIN - Département Systèmes et Circuits Intégrés Numériques : DRT/LIST/DSCIN
Abstract : This paper addresses the problem of target localization in sonar signal in a 2D (range-azimuth) scene. The aim is to propose an approach based on an artificial neural network that outputs a binary occupancy grid. A dataset is generated using a sonar simulator and used to train and validate a deep neural network based on a U-net architecture. A pre-processing chain converts analog data to a form that can be passed through the neural network, in this case a (range-azimuth) 2D map with power received. Finally, the performances of the network are compared to those of an approach built around on a CFAR-based range estimation and a MUSIC-based direction of arrival estimation. The results show that the network is able to provide at least similar performances than the reference approach, without the algorithmic calibration currently required by the latter.
Document type :
Conference papers
Complete list of metadata
Contributor : Carolynn Bernier Connect in order to contact the contributor
Submitted on : Thursday, December 16, 2021 - 10:44:56 AM
Last modification on : Saturday, February 19, 2022 - 3:13:48 AM
Long-term archiving on: : Thursday, March 17, 2022 - 6:21:42 PM


Files produced by the author(s)


  • HAL Id : cea-03482704, version 1


Q Bruel, F Heitzmann, D Morche, J Huillery, E Blanco, et al.. Using deep learning for sonar targets localization. ASPAI'2020 - 2nd International Conference on Advances in Signal Processing and Artificial Intelligence, IFSA, Jun 2020, berlin, Germany. ⟨cea-03482704⟩



Record views


Files downloads