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

doi:10.12382/bgxb.2023.0395

摘要/Abstract

摘要：

舰船辐射噪声信号是感知目标舰船的重要方式之一。诱饵信号通过模仿目标舰船辐射噪声,混淆感知系统以掩护目标舰船完成战略目标。如果可以通过对舰船辐射噪声与诱饵信号识别方式实现抗干扰,将大大提高战时敌方舰船识别效率,从而提高战术行动的效率和成功率。将抗诱饵干扰问题转化为单类分类问题,尝试采用深度学习范式给出解决方法。以此为背景,提出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。构建针对舰船辐射噪声信号时域、时频域数据的对抗生成网络结构。采用对抗训练策略捕捉舰船辐射噪声信号的时域、时频域联合分布,提高模型针对舰船辐射噪声信号的表示学习能力,最终实现端到端的舰船辐射噪声信号单类分类任务。实验数据由外场实验模拟产生诱饵信号采集而来。研究结果表明:在无监督条件下的AUC值为0.84,相对于单类分类基线模型高出0.17,可以实现对舰船辐射噪声信号抗诱饵干扰。

关键词: 舰船辐射噪声, 抗诱饵信号干扰, 对抗生成网络, 单类分类

Abstract:

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.

Key words: ship-radiated noise, anti-decoy jamming, generative adversarial network, one-class classification

中图分类号:

TN911

段一琛, 申晓红, 王海燕, 闫永胜. 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[J]. 兵工学报, 2023, 44(9): 2722-2731.

DUAN Yichen, SHEN Xiaohong, WANG Haiyan, YAN Yongsheng. An Anti-decoy Jamming Method of Ship-radiated Noise Signals Based on Unsupervised Time-frequency Information Fusion[J]. Acta Armamentarii, 2023, 44(9): 2722-2731.

图/表 8

图1 本文深度学习模型结构示意图

Fig.1 Overview of model architecture

图2 GF结构示意图

Fig.2 Structure of time-frequency domain generator GF

图3 GT结构示意图

Fig.3 Structure of time domain generator GT

图4 部分样本时域时频域可视化

Fig.4 Visualization of experimental data samples

表1 消融实验结果

Table 1 Ablation experimental results

模型	AUC
模型1	0.848 6
模型2	0.764 5
模型3	0.798 3

图5 消融实验T-SNE可视化

Fig.5 T-SNE visualization of ablation experiments

图6 消融实验模型输出分布

Fig.6 Model output distribution for ablation experiments

表2 对比实验结果

Table 2 Comparison of experimental results

模型	AUC
ALAD^[42]	0.645 1
ANIGD^[43]	0.799 2
DASVDD^[44]	0.723 4
DeepSVDD^[45]	0.679 8
GANOMALY^[46]	0.651 3
SKIPGAN^[47]	0.765 5
COCA^[48]	0.802 2
本文方法	0.848 6

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