Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder

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.