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.

船舶辐射噪声信号是检测和识别船舶的重要手段之一，发射干扰噪声屏蔽自身辐射噪声信号是常用的对策。本文尝试利用信号增强的思想，对信噪比极低的舰船辐射噪声信号进行增强，从而达到抗爆信号干扰的目的。我们提出了一种信号增强深度学习模型，通过在时域中学习掩码来增强船舶辐射噪声信号。我们的方法是带有 U-net 的编码器-解码器结构。U-net 由带有跳过连接的 1d-conv 组成。为了提高模型的学习能力，我们直接将U-net串联起来。为了提高模型对时间序列信息的学习能力。采用Transformer注意力机制使模型具有学习时间信息的能力。我们提出了一种组合损失函数，用于时域中的尺度不变源噪声比和均方误差。最后，我们使用实际收集的数据进行实验。经验证，在−20 dB至−25 dB的极低信噪比下，我们的算法可以有效地将船舶辐射噪声信号的信噪比提高到2 dB。