Single-vector hydrophone orientation estimation method based on deep learning

The invention belongs to the technical field of underwater acoustic physics and hydrophone orientation estimation, and particularly relates to a single-vector hydrophone orientation estimation method based on deep learning, which comprises the following steps of: preprocessing actually measured data which are received by a single-vector hydrophone and are not provided with labels to obtain preprocessed data; and inputting the preprocessed data into the trained deep learning neural network model to obtain a label corresponding to the preprocessed data as an azimuth angle of a sound source, thereby completing single-vector hydrophone azimuth estimation. 本发明属于水声物理和水听器方位估计技术领域，具体涉及一种基于深度学习的单矢量水听器方位估计方法，该方法包括：对单矢量水听器接收的不带标签的实测数据进行预处理，获得预处理后的数据；将预处理后的数据输入至训练好的深度学习神经网络模型，获得预处理后的数据对应的标签，作为声源的方位角，完成单矢量水听器方位估计。