Physics-based Data Augmentation for Machine Learning (ML) Models

For imagery and vocal audio, tools exist to allowÝindividuals to generate realistic audio and video clips, also known as deep fakes. Despite advances in deep fake technology for audio-visual content, the naval sonar domain lacks comparable synthetic data generation capabilities. This limitation impedes the development of AI/ML algorithms for sonar applications, as obtaining real-world training data is cost-prohibitive and existing physics-based simulations are computationally intensive. The proposed research addresses this gap by developing an AI-enabled tool for generating physics-based synthetic sonar data across the mid-frequency range (1-10 kHz).Altronís solution leverages generative adversarial networks and deep predictive coding models to synthesize realistic sonar signatures. By combining limited recorded exemplars from naval exercises with physics-based contact attributes, the system aims to generate diverse, high-fidelity datasets representing various bathymetric conditions, weather patterns, volume scattering effects, and contact density scenarios. This approach would overcome the current shortage of training data, particularly from conflict situations with realistic enemy contact distributions.Success metrics include computational efficiency, image similarity measurements, and user evaluation of synthetic data quality. The primary application is training AI/ML algorithms for enhanced target detection, classification, and tracking systems. This innovation would enable rapid deployment of advanced sonar capabilities while addressing the fundamental challenge of limited training data availability in the naval domain.