An open-set recognition method for ship radiated noise signal based on graph convolutional neural network prototype learning

The underwater acoustic perception system often undertakes multi-class ship recognition tasks. As the underwater acoustic recognition environment becomes increasingly complex, underwater acoustic perception systems often face various interference, such as unknown ships radiated noise signals and decoy signals. This poses higher requirements on the robustness and recognition capabilities of underwater acoustic target recognition methods. In this paper, we transform the problem of underwater acoustic target recognition in this scenario into an open-set recognition problem. We design a deep learning model based on graph convolutional neural networks, propose a graph embedding method for time-domain ships radiated noise signals, and propose a fully parameterized prototype learning framework. We simulate decoy signals in real sea areas, and all the data used in the experiments are actual collected data. The fully parameterized prototype learning framework based on a data-driven approach can not only effectively resist interference from unknown ship-radiated noise signals and decoy signals, but also accurately identify multi-class target ship-radiated noise signals. Ultimately, our method achieves end-to-end open-set recognition of ship-radiated noise signals.