Advancement of the Pressure Variation Model for Improved State Estimation in Underwater Vehicles

Unmanned underwater vehicles (UUVs) are essential tools for marine exploration, research, and surveillance. Accurate state estimations are critical for effective navigation, but conventional methods, such as Doppler velocity logs (DVLs) and inertial navigation systems, are expensive and vulnerable to environmental conditions. Inspired by the biological lateral line system in fish, this study proposes an enhanced pressure variation model (PVM) that estimates the velocity and drift angles using the data from pressure sensors. The improved model introduces unified regression coefficients and accounts for nonlinear flow effects, reducing the reliance on motion-specific parameters and increasing the adaptability across various maneuvering conditions. The model was validated by conducting extensive computational fluid dynamics (CFD) simulations across multiple motion scenarios. The enhanced PVM achieved high estimation accuracy while maintaining robustness under different dynamic conditions. The contributions of this study include the following: (1) a refined estimation framework using a unified coefficient model, (2) a low-cost, environment-resilient alternative to traditional systems, and (3) verified reliability through CFD-based performance evaluations. Future work will focus on experimental validation, extending the performance to large-angle motions, and developing a real-time data processing module to enable in situ application. This approach supports more autonomous and reliable UUV navigation for marine robotics and underwater missions.