Small sample vertical array target distance estimation method based on transfer learning

The invention relates to a small sample vertical array target distance estimation method based on transfer learning, and a convolutional neural network is one of deep neural networks and is widely applied to classification and positioning of underwater acoustic targets. For different fields, a traditional machine learning independently trains a model and cannot be directly applied to other environments. In order to make full use of a large amount of underwater sound data of a known sea area, migrate the data to a strange sea area and realize distance estimation of a target sound source under strong interference, the invention provides a sound source distance estimation method of a migration learning model based on a convolutional neural network. The method comprises the following steps: establishing a transfer learning model by taking the complex sound pressure of a sound pressure field as a feature, training a large number of samples of the known sea area by using the convolutional neural network, establishi