Dynamic Prediction of Full-Ocean Depth SSP by a Hierarchical LSTM: An Experimental Result

Sound speed profile (SSP) distribution is an important parameter for underwater positioning, navigation, and timing (PNT) because it affects the propagation mode of underwater acoustic signals. To accurately predict future sound speed distribution, we propose a hierarchical long short-term memory (H-LSTM) neural network, which explores the distribution pattern of sound velocity in the time dimension. To verify the feasibility and effectiveness, we conducted both simulations and real experiments. The ocean experiment was held in the South China Sea in April 2023. Results show that the predictive error of the proposed method is only 0.1530 m/s, demonstrating the proposed method outperforms the state-of-the-art methods and providing additional evidence of its heightened predictive capability in predicting future sound speed distribution.