Sonar Foundational Model for Representation Learning and Automatic Target Recognition Systems in Underwater Maritime Environment

Advances in deep learning have not yet led to a practical revolution in underwater Automatic Target Recognition using passive sonar, in contrast to deep learning's success in other application domains. This is at least in part because the diversity and complexity of sonar data combined with relatively limited datasets has not yet led to deep learning systems that outperform traditional signal processing approaches reliably across a range of underwater environments.

This project aims to achieve a breakthrough in passive sonar ATR by developing a foundational model for passive sonar. First, it will aggregate existing sonar datasets to train a more general and robust sonar representation. Secondly, it will study how this representation can support data-efficient learning for ATR, tracking, etc as well as domain generalisation techniques to guarantee generalisation across different oceanic conditions and hydrophone types.