Attentive Neural Processes for Fast Trajectory Prediction in Underwater Acoustic Networks

Accurate underwater localization is crucial for autonomous underwater vehicles, yet it remains challenging due to the complex and dynamic nature of marine environments. Traditional localization methods that rely on anchor nodes and precise synchronization often fail to adapt to real-world underwater acoustic channel conditions characterized by multipath effects, high attenuation, and significant variability. Recent advances in deep learning offer model-free approaches that mitigate some of these issues, but they still struggle with generalization in scenarios with limited data. In this study, we introduce the application of attentive neural processes (ANPs) to underwater localization, enhancing both accuracy and robustness through effective uncertainty modeling and dynamic adaptation based on observed context data. By leveraging the ANP framework, our approach paves the way for extensive offline pretraining on simulated data, followed by rapid few-shot adaptation under real conditions; in this work, we evaluate near-zero-shot generalization. This strategy not only improves predictive performance but also significantly reduces the need for large-scale real-world data, making it highly suitable for dynamic underwater environments. Extensive evaluations demonstrate that ANPs significantly outperform traditional multilayer perceptron approaches, achieving reductions in mean absolute error of up to 25% compared to standard baselines. These results highlight the potential of ANPs to deliver reliable, adaptable, and precise localization solutions in challenging underwater scenarios.