Physics-Informed Synthetic Sonar Data Generation using Deep Diffusion Models

The need for synthetic sonar data generation has become increasingly critical as it enables the expansion of sonar applications beyond the limitations of existing test regions. Current systems, which rely on clustered feature data and random forest classification, are constrained by the geographic and environmental boundaries of available data. To address these limitations, we propose a novel, physics-informed synthetic data generation method using AI. The proposed Phase I Technical Effort will focus on the proof of concept development and evaluation of a physics-informed generative deep learning framework for synthetic sonar data generation that will address the limitations of current systems.Our approach transforms sonar detection frequency clusters into padded image representations, enabling them to be processed through a custom diffusion-style architecture. Inspired by Denoising Diffusion Probabilistic Models (DDPM), the diffusion model progressively refines noise into structured outputs. The loss function incorporates real-world sonar propagation equations, ensuring the generated data realistically aligns with in-situ sonar conditions. Environmental parameters, including water depth, temperature, and sea state, are integrated as conditional inputs to guide the synthetic data generation process. Due to the lack of a readily available sonar data, a representative dataset will be selected for the Phase I framework development; however, development will be conducted with flexibility and broad applicability in mind such that the architecture can be adapted to acoustic sonar data in the Phase II effort.This physics-driven system will result in the creation of realistic synthetic sonar data when given desired environmental conditions. As a result, data will be generated for regions outside existing test areas, facilitating comprehensive testing, training, and operational deployment of AI-enabled sonar systems. By existing datasets and recorded environmental conditions, this system will greatly enhance NAVSEAís capabilities by providing realistic data for diverse operational scenarios, thus addressing critical gaps in current sonar data generation systems.