Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder

Alberto Testolin, Roee Diamant

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Detecting and tracking moving targets is a challenging task, which becomes even harder in underwater scenarios due to the extremely low levels of signal-to-noise ratio associated with common acoustic measures. In the context of continuous marine monitoring, a further challenge is provided by the need to deploy computationally efficient methods that guarantee minimum use of power resources in off-shore monitoring platforms. Here we present a novel approach to accurately detect and track moving targets from the reflections of an active acoustic emitter. Our system is based on a computationally- and energy-efficient deep convolutional denoising autoencoder. System performance is evaluated both on simulated and emulated data, and benchmarked against a probabilistic tracking method based on the Viterbi algorithm.

Original languageEnglish
Title of host publication2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages281-285
Number of pages5
ISBN (Electronic)9781728155494
DOIs
StatePublished - Dec 2019
Event8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Le Gosier, Guadeloupe
Duration: 15 Dec 201918 Dec 2019

Publication series

Name2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings

Conference

Conference8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019
Country/TerritoryGuadeloupe
CityLe Gosier
Period15/12/1918/12/19

Bibliographical note

Funding Information:
A. Testolin is with the Department of Information Engineering and the Department of General Psychology, University of Padova, Italy. Email: alberto.testolin@unipd.it; R. Diamant is with the Department of Marine Technologies, University of Haifa, Israel. Email: roee.d@univ.haifa.ac.il This work was funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 773753 (SYMBIOSIS). A.T. gratefully acknowledges the support of the NVIDIA Corporation for the donation of a Titan Xp GPU used for this research.

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Convolutional Neural Networks
  • Denoising autoencoders
  • Marine monitoring
  • Signal detection
  • Underwater acoustics
  • Underwater tracking
  • Viterbi algorithm

ASJC Scopus subject areas

  • Control and Optimization
  • Artificial Intelligence
  • Computer Networks and Communications

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