基于迁移学习的小样本垂直阵目标距离估计方法

To address the problem of target distance estimation in the sea area with only a small amount of underwater acoustic data, this paper takes the real and the imaginary parts of the complex sound pressure of the sound field as the characteristics, constructs a transfer learning model, and retrains the small sample underwater acoustic data in the exploration sea area on the basis of pretraining a large amount of underwater acoustic data in the preselected sea area by using the convolution neural network to realize the underwater sound source distance estimation under the small sample underwater acoustic data. The S5 voyage data in the SWellEX-96 experiment without any strong interference and S59 voyage data with a strong interference are used to verify the performance of the method. Distance estimation is achieved for shallow and deep sources in the experiment. The range estimation performance of the underwater target sound source by matching field processing, traditional convolution neural network, and transfer learning methods are compared. Results show that the transfer learning model based on a convolution neural network can provide good distance estimations in both environments with and without strong interference. Moreover, the estimation performance is significantly better than traditional convolutional neural networks and matching field processing.