无监督时频信息结合的舰船辐射噪声信号抗诱饵干扰方法

Ship radiated noise signals gives one of the important ways to perceive the target ship. Decoy signals confuse the perception system by imitating the target ship radiated noise to cover the target ship and complete the strategic goal. If anti-decoy jamming can be realized through the recognition of ship radiated noise signals and decoy signals, the efficiency of enemy ship identification in wartime can be greatly improved, so as to improve the efficiency and success rate of tactical operations. The anti-decoy jamming problem is transformed into a one-class classification problem, and the deep learning method is attempted to propose a solution. Against this background, this paper proposes an unsupervised time-frequency information fusion method for anti-decoy jamming of ship radiated noise signals. The generative adversarial network structure for the time domain and time-frequency domain data of the ship radiated noise signal is constructed. The adversarial training strategy is used to capture the time domain and time-frequency domain information of the ship radiated noise signal, which improves the representation learning ability of the model. Finally, the end-to-end one-class classification task of ship radiated noise can be realized. The experimental data are collected from the decoy signals generated by the simulation of the external field experiment. The AUC in the unsupervised condition is 0. 84, which is 0. 17 higher than the baseline model for one-class classification. The experimental results show that the method can achieve anti-decoy jamming for the ship radiated noise signal.