Estimating the target position of low-frequency sound sources in a shallow sea environment is difficult due to the high cost of hydrophone placement and the complexity of the propagation model. We propose a compressed recurrent neural network (C-RNN) model that compresses the signal received by a vector hydrophone into a dynamic sound intensity signal and compresses the target position of the sound source into a GeoHash code. Two types of data are used to carry out prior training on the recurrent neural network, and the trained network is subsequently used to estimate the target position of the sound source. Compared with traditional mathematical models, the C-RNN model functions independently under the complex sound field environment and terrain conditions, and allows for real-time positioning of the sound source under low-parameter operating conditions. Experimental results show that the average error of the model is 56 m for estimating the target position of a low-frequency sound source in a shallow sea environment.

由于水听器放置的高成本和传播模型的复杂性，在浅海环境中估计低频声源的目标位置很困难。我们提出了一种压缩递归神经网络（C-RNN）模型，该模型将矢量水听器接收的信号压缩为动态声强信号，并将声源的目标位置压缩为GeoHash代码。两种类型的数据用于在递归神经网络上进行事先训练，而训练后的网络随后用于估计声源的目标位置。与传统的数学模型相比，C-RNN模型在复杂的声场环境和地形条件下可以独立运行，并允许在低参数操作条件下实时定位声源。实验结果表明，该模型在浅海环境中估计低频声源目标位置的平均误差为56 m。