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SSP distribution is an important parameter for underwater positioning,navigation and timing (PNT) because it affects the propagation mode ofunderwater acoustic signals. To accurate predict future sound speeddistribution, we propose a hierarchical long short--term memory (H--LSTM)neural network for future sound speed prediction, which explore thedistribution pattern of sound velocity in the time dimension. To verify thefeasibility and effectiveness, we conducted both simulations and realexperiments. The ocean experiment was held in the South China Sea in April,2023. Results show that the accuracy of the proposed method outperforms thestate--of--the--art methods.

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