Data augmentation based on highlight image models of underwater maneuvering target

Abstract

With the development of underwater acoustic countermeasure technology, deep learning is applied to recognize echo geometry features of underwater targets, but it faces the problem of sample scarcity. In this paper, we improved the underwater target highlight model, and established the target echo information equation of active sonar. By changing the spatial positions of target and sonar regularly, we performed the highlight image models of underwater maneuvering targets. Taking an underwater vehicle as an example, the model construction process was introduced in detail, and highlight image models of four typical acoustic scale decoys were also established, and five multi-space state highlight image data samples were generated. The eHasNet-5 convolutional classification network was designed, and the network was trained, verified and tested with the generated data. Finally, the experimental data test shows that the target highlight image generation models provide a new data augmentation method for the application of deep learning in active sonar target recognition, and the trained network by generated data has the ability to classify two-dimensional objects.