cs.SD - 2023-10-14

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

• paper_url: http://arxiv.org/abs/2310.09522
• repo_url: None
• paper_authors: Jiajun Lu, Wei Huang, Hao Zhang
• for: 用于预测未来水声速度分布，提高海上定位、导航和时间测量（PNT）精度。
• methods: 提议使用层次Long Short-Term Memory（H-LSTM）神经网络预测未来水声速度分布，利用时间维度中的声速分布分布模式。
• results: 通过实验和仿真 validate the proposed method，结果显示该方法的准确率高于现有方法。
  Abstract

  SSP distribution is an important parameter for underwater positioning, navigation and timing (PNT) because it affects the propagation mode of underwater acoustic signals. To accurate predict future sound speed distribution, we propose a hierarchical long short--term memory (H--LSTM) neural network for future sound speed prediction, which explore 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 accuracy of the proposed method outperforms the state--of--the--art methods.
  摘要

  <>Note that the translation is in Simplified Chinese, which is the standard writing system used in mainland China. If you prefer Traditional Chinese, I can provide that as well.