An Anti-decoy Jamming Method of Ship-radiated Noise Signals Based on Unsupervised Time-frequency Information Fusion

doi:10.12382/bgxb.2023.0395

Abstract

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

CLC Number:

TN911

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.

Figures/Tables 8

Fig.1 Overview of model architecture

Fig.2 Structure of time-frequency domain generator GF

Fig.3 Structure of time domain generator GT

Fig.4 Visualization of experimental data samples

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

Fig.5 T-SNE visualization of ablation experiments

Fig.6 Model output distribution for ablation experiments

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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