Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder

By: Testolin A., Diamant R.
Published in: 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 – Proceedings
SDGs : SDG 14  |  Units: Marine Sciences  | Time: 2019 |  Link
Description: Detecting and tracking moving targets is a challenging task, which becomes even harder in underwater scenarios due to th e 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. © 2019 IEEE.