@inproceedings{fb0084a164504325bfea91f7a3076eac,
title = "Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder",
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 = "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",
year = "2019",
month = dec,
doi = "https://doi.org/10.1109/CAMSAP45676.2019.9022594",
language = "American English",
series = "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",
booktitle = "2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings",
address = "United States",
}