Learning-Based Leader Localization for Underwater Vehicles With Optical-Acoustic-Pressure Sensor Fusion

Underwater vehicles have emerged as a critical technology for exploring aquatic environments. The deployment of multivehicle systems has gained substantial interest due to their capability to perform collaborative tasks with improved efficiency. However, achieving precise localization of a leader underwater vehicle within a multivehicle configuration remains a significant challenge, particularly in dynamic and complex underwater conditions. To address this issue, this article presents a novel trimodal sensor fusion neural network approach that integrates optical, acoustic, and pressure sensors to localize the leader vehicle. The proposed method leverages the unique strengths of each sensor modality to improve localization accuracy and robustness. Specifically, optical sensors provide high-resolution imaging for precise relative positioning, acoustic sensors enable long-range detection and ranging, and pressure sensors offer environmental context awareness. The fusion of these sensor modalities is implemented using a deep learning architecture designed to extract and combine complementary features from raw sensor data. The effectiveness of the proposed method is validated through a custom-designed testing platform and field test. Extensive data collection and experimental evaluations demonstrate that the trimodal approach significantly improves the accuracy and robustness of leader localization, outperforming both single-modal and dual-modal methods.