Terrain-Aided Navigation for Autonomous Underwater Vehicles
Summary
Terrain-aided navigation integrates high-resolution bathymetric data with on-board sensors to enable autonomous underwater vehicles to determine their position in GPS-denied environments. By matching real-time depth or altitude measurements against digital elevation models of the seafloor, vehicles can correct inertial drift and maintain accurate trajectories over extended missions. Architectures typically combine inertial navigation systems, Doppler velocity logs and sonar altimeters within probabilistic frameworks such as particle filters or extended Kalman filters. Advances in data processing algorithms, sensor fusion and map generation have extended operational range and reliability, mitigating challenges posed by flat or ambiguous terrain. Implementations span deep-sea exploration, under-ice surveys, pipeline inspection and environmental monitoring, underscoring global significance in oceanographic research, resource exploitation and maritime security.
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Terrain-Aided Navigation for Autonomous Underwater Vehicles publication trend
The graph below shows the total number of articles in terrain-aided navigation for autonomous underwater vehicles across all publications each year (not limited to Nature Index journals).
Technical terms
Terrain-Aided Navigation (TAN): A method of underwater localisation that compares measured depth or altitude with pre-existing seabed models to estimate position.
Digital Elevation Model (DEM): A dataset representing the three-dimensional shape of the seafloor used as reference for TAN.
Particle Filter: A probabilistic algorithm that represents possible vehicle states as particles, iteratively weighting and resampling them against measurement likelihoods.
Doppler Velocity Log (DVL): An acoustic sensor that measures velocity relative to the seabed or water column, aiding dead-reckoning and TAN.
Bathymetric Chart: A map of underwater topography, providing the baseline terrain data required for TAN algorithms.
References
- An Effective Terrain Aided Navigation for Low-Cost Autonomous Underwater Vehicles. Sensors (2017).
- Improvements to Terrain Aided Navigation Accuracy in Deep-Sea Space by High Precision Particle Filter initialization. IEEE Access (2019).
- Study on the Arctic Underwater Terrain-Aided Navigation Based on Fuzzy-Particle Filter. International Journal of Fuzzy Systems (2021).
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