Bridging Depths: Dynamic Geometric Proxies and Neural Networks for Robust Oceanic Feature Representation

Robust feature representation poses significant challenges in autonomous sonar target recognition, exacerbated by unpredictable acoustic scatter, underwater multipath effects, and uncertainty in target geometry. Although machine learning techniques have been widely applied, there remains a gap in the numerical and theoretical studies addressing these complex morphing geometries. Specifically, the impact of nonlinear overlapping and dynamically varying parameters on surrounding geometries remains unexplored. This position paper explores the potential of dynamic geometric proxies, human features, and other natural patterns to tackle the challenges of robust feature representation in autonomous sonar target recognition. Using proxies such as braids, knots, and links, we aim is to establish reliable ground truths to interpret sonar features. Our methodology involves extending to state-of -the-art (SOTA) architectures such as Vision Transformers (ViTs) utilizing standard CNN backbones such as ResNet to extract distinctive features from filtered ping responses and geometrically represent them. Additionally, we explore the augmentation of datasets by generating geometric proxies from human features that exhibit similar geometric patterns. Recent research indicates that incorporating dynamic human features as geometric proxies for acoustic representation in the ocean can broaden sonar applications to include signal processing for autonomous target recognition. To realize this potential, this study recommends the creation and collection of target category signatures, as well as their geometric structures. The given signatures will be converted into powerful class/category features using the trained Vision Transformer model. Our approach proposes a novel perspective on feature extraction for ocean-related applications, aiming to improve underwater signal processing and acoustic analysis. By developing geometric proxies and training neural networks based on sound ground truths, this research is expected to significantly advance sonar target recognition capabilities significantly.