Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder

Alberto Testolin; Roee Diamant

doi:10.1109/CAMSAP45676.2019.9022594

Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder

Alberto Testolin, Roee Diamant

Department of Marine Technologies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	281-285
Number of pages	5
ISBN (Electronic)	9781728155494
DOIs	https://doi.org/10.1109/CAMSAP45676.2019.9022594
State	Published - Dec 2019
Event	8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Le Gosier, Guadeloupe Duration: 15 Dec 2019 → 18 Dec 2019

Publication series

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

Conference

Conference	8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019
Country/Territory	Guadeloupe
City	Le Gosier
Period	15/12/19 → 18/12/19

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CAMSAP45676.2019.9022594

Cite this

Testolin, A., & Diamant, R. (2019). Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder. In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings (pp. 281-285). [9022594] (2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAMSAP45676.2019.9022594

Testolin, Alberto ; Diamant, Roee. / Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder. 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 281-285 (2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings).

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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.",

keywords = "Convolutional Neural Networks, Denoising autoencoders, Marine monitoring, Signal detection, Underwater acoustics, Underwater tracking, Viterbi algorithm",

author = "Alberto Testolin and Roee Diamant",

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{\textquoteright}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: {\textcopyright} 2019 IEEE.; 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 ; Conference date: 15-12-2019 Through 18-12-2019",

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Testolin, A & Diamant, R 2019, Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder. in 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings., 9022594, 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 281-285, 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019, Le Gosier, Guadeloupe, 15/12/19. https://doi.org/10.1109/CAMSAP45676.2019.9022594

Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder. / Testolin, Alberto; Diamant, Roee.

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 281-285 9022594 (2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Diamant, Roee

N1 - 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.

PY - 2019/12

Y1 - 2019/12

N2 - 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.

AB - 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.

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KW - Denoising autoencoders

KW - Marine monitoring

KW - Signal detection

KW - Underwater acoustics

KW - Underwater tracking

KW - Viterbi algorithm

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M3 - Conference contribution

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EP - 285

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019

Y2 - 15 December 2019 through 18 December 2019

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Testolin A, Diamant R. Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder. In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 281-285. 9022594. (2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings). doi: 10.1109/CAMSAP45676.2019.9022594

Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this