AbstractSeagliders® are buoyancy-driven autonomous underwater vehicles whose sub-surface position estimates are typically derived from velocities inferred using a flight model. We present a method for computing velocities and positions during the different phases typically encountered during a dive-climb profile based on a buoyancy-driven flight model. We compare these predictions to observations gathered from a Seaglider deployment on the acoustic tracking range in Dabob Bay (200 m depth, mean vehicle speeds ~30 cm s-1), permitting us to bound the position accuracy estimates and understand sources of various errors. We improve position accuracy estimates during long vehicle accelerations by numerically integrating the flight-model's fundamental momentum-balance equations. Overall, based on an automated estimation of flight-model parameters, we confirm previous work that predicted vehicle velocities in the dominant dive and climb phases are accurate to < 1 cm s-1, which bounds the accumulated position error in time. However, in this energetic tidal basin, position error also accumulates due to unresolved depth-dependent flow superimposed upon an inferred depth-averaged current.
An Ultra-Short Baseline Positioning Model Based on Rotating Array & Reusing Elements and Its Error Analysis
