SA-MDF-CNN fuses remote sensing SST data with historical SSP characteristics and coordinates via CNNs and self-attention to estimate underwater sound speed profiles with lower RMSE than prior methods.
Isac- enabled underwater iot network localization: Overcoming a synchrony, mobility, and stratification issues
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An Attention-Assisted Multi-Modal Data Fusion Model for Real-Time Estimation of Underwater Sound Velocity
SA-MDF-CNN fuses remote sensing SST data with historical SSP characteristics and coordinates via CNNs and self-attention to estimate underwater sound speed profiles with lower RMSE than prior methods.