Application of the sound speed profile and sound source location in shallow waters [循环神经网络在浅海声速-声源联合反演中的应用]

作者：Wang T.; Su L.^*; Ren Q.; Wang W.; Ma L.

来源：Harbin Gongcheng Daxue Xuebao/journal of Harbin Engineering University, 2021, 42(8): 1133-1139.

DOI：10.11990/jheu.202007057

• Ensemble Kalman filtering
• Machine learning
• Recurrent network
• Sequential filtering
• Sound speed profile
• Sound speed profile inversion
• Underwater acoustic localization
• Underwater target

The time-variant characteristics of the sound speed profile are usually described as a first-order random walk process when the sequential filtering approach is utilized for the time-varying sound speed profile inversion. To make state quotations better predict state variables, this paper uses the recurrent neural network to analyze historical hydrographic data and models for the time-varying sound speed profile in shallow water, thus accurately describing the time-variant characteristics of the sound speed profile. Based on this, Ensemble Kalman Filtering is used for the sound speed profile inversion and sound source location. The method improved results are obtained when compared with the joint inversion of the first-order random walk process. The root mean square error of sound source depth is reduced by 80%, and the error of sound speed profile inversion result is reduced by 38.2%. Feasibility of the method is verified by simulated acoustic data of the measured sound speed profile in this paper.

• 单位
  中国科学院大学