Estimation of Underwater Sound Speed Profile via Meta Learning with Data-driven Learning Rate: An Experimental Result

Rapid and accurate estimation of sound speed profile (SSP) is of great importance for underwater precise positioning and navigation, as the propagation mode of sound signals is influenced by the distribution of sound speed. Among the existing methods for estimating SSPs, artificial neural networks (ANN) have better real-time advantages compared to matched field processing (MFP). However, under few-shot learning situations, they are prone to be overfitting and the inversion accuracy decreases. To achieve accurate SSP inversion in few-shot learning, we propose a spatio-temporal information driven meta learning (ST-ML) method. By learning different types of SSP distribution, common features are extracted, thus accelerating the learning process on given tasks, and reducing the demand for reference samples, so as to improve the accuracy of SSP inversion in few-shot learning situation. To verify the feasibility and effectiveness of ST-ML, a deep-ocean experiment was held in April 2023. Results show that ST-ML outperforms the state-of-the-art methods in terms of accuracy for SSP inversion, while inherits the real-time advantage of ANN during the inversion stage.