Ship radiated noise signal is one of the important ways to detect and identify ships, and emission of interference noise to shield its own radiated noise signal is a common countermeasure. In this paper, we try to use the idea of signal enhancement to enhance the ship radiated noise signal with extremely low signal-to-noise ratio, so as to achieve anti-explosive signal interference. We propose a signal enhancement deep learning model to enhance the ship radiated noise signal by learning a mask in the temporal domain. Our approach is an encoder–decoder structure with U-net. U-net consists of 1d-conv with skip connection. In order to improve the learning ability of the model, we directly connect the U-net in series. In order to improve the learning ability of the model’s time series information. The Transformer attention mechanism is adopted to make the model have the ability to learn temporal information. We propose a combine Loss function for scale-invariant source-to-noise ratio and mean squared error in time-domain. Finally, we use the actual collected data to conduct experiments. It is verified that our algorithm can effectively improve the signal-to-noise ratio of the ship radiated noise signal to 2 dB under the extremely low signal-to-noise ratio of − 20 dB to − 25 dB.

ship radiated noise signal is one of the important ways to detect and identify ships, and emission of interference noise to shield its own radiated noise signal is a common countermeasure. In this paper, we try to use the idea of signal enhancement to enhance the ship radiated noise signal with extremely low signal-to-noise ratio, so as to achieve anti-explosive signal interference. We propose a signal enhancement deep learning model to enhance the ship radiated noise signal by learning a mask in the temporal domain. Our approach is an encoder-decoder structure with U-net. U-net consists of 1d-conv with skip connection. In order to improve the learning ability of the model, we directly connect the U-net in series. In order to improve the learning ability of the model's time series information, we avoid deep learning paradigms such as lstm or RNN with high computational complexity. The Transformer attention mechanism is adopted to make the model have the ability to learn temporal information. We propose a combine Loss function for SI-SNR and mean squared error in time-domain. Finally, we use the actual collected data to conduct experiments. It is verified that our algorithm can effectively improve the signal-to-noise ratio of the ship radiated noise signal to 2dB under the extremely low signal-to-noise ratio of -20dB to -25dB.

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