Search Results
You are looking at 1 - 1 of 1 items for
- Author or Editor: Lora J. Van Uffelen x
- Refine by Access: All Content x
Assessing Seaglider Model-Based Position Accuracy on an Acoustic Tracking RangeAbstract
Seagliders are buoyancy-driven autonomous underwater vehicles whose subsurface 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.
Abstract
Seagliders are buoyancy-driven autonomous underwater vehicles whose subsurface 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.
