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Direct regressions for underwater acoustic source localization in fluctuating oceans

Riwal Lefort 1, 2 Gaultier Real 3 Angélique Drémeau 1, 2
1 Lab-STICC_ENSTAB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
2 Pôle STIC_AP
ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne
Abstract : In this paper, we show the potential of machine learning regarding the task of underwater source localization through a fluctuating ocean. Underwater source localization is classically addressed under the angle of inversion techniques. However, because an inversion scheme is necessarily based on the knowledge of the environmental parameters, it may be not well adapted to a random and fluctuating underwater channel. Conversely, machine learning only requires using a training database, the environmental characteristics underlying the regression models. This makes machine learning adapted to fluctuating channels. In this paper, we propose to use non linear regressions for source localization in fluctuating oceans. The kernel regression as well as the local linear regression are compared to typical inversion techniques, namely Matched Field Beamforming and the algorithm MUSIC. Our experiments use both real tank-based and simulated data, introduced in the works of Real et al. Based on Monte Carlo iterations, we show that the machine learning approaches may outperform the inversion techniques.
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https://hal.archives-ouvertes.fr/hal-01656430
Contributeur : Annick Billon-Coat <>
Soumis le : mardi 5 décembre 2017 - 16:21:32
Dernière modification le : mercredi 24 juin 2020 - 16:19:51

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Riwal Lefort, Gaultier Real, Angélique Drémeau. Direct regressions for underwater acoustic source localization in fluctuating oceans. Applied Acoustics, Elsevier, 2017, Applied Acoustics, 116, pp.303-310. ⟨10.1016/j.apacoust.2016.10.005⟩. ⟨hal-01656430⟩

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