基于ViT网络模型自注意力机制的干扰类型分布式识别方法

doi:10.12382/bgxb.2025.0127

摘要/Abstract

摘要：

针对传统集中式大功率干扰在现代通信对抗中存在覆盖有限、易被定位等问题，提出一种基于空海一体化架构的分布式通信干扰信号识别方法。该方法通过构建空海协同电子对抗体系，由空中无人机采集干扰信号，海上信息处理中心进行多源信号融合与特征提取，并设计融合多微信号表征的多阶段Vision Transformer识别模型，实现干扰信号的自动识别与分类。实验结果表明：该方法能够有效提升干扰信号的识别精度与效率，为通信系统的抗干扰决策提供支持。研究结论显示，该方法能够有效提升分布式通信干扰信号的识别精度与效率，具备较强的鲁棒性和适应性，能够满足现代战场对快速识别干扰信号的需求，并为分布式通信干扰技术的工程化应用提供新的可行路径，为空海一体化电子对抗体系的构建奠定技术基础。

关键词: 分布式通信干扰, 空海一体化, 信号识别, Vision Transformer

Abstract:

The centralized high-power jamming has the limitations，such as restricted coverage and susceptibility to localization，in modern communication countermeasures.This paper presents a distributed jamming signal recognition method based on an air-sea integrated framework.The proposed method is to use the unmanned aerial vehicles to collect the jamming signals，while a maritime information center conducts multi-source fusion and feature extraction.A multi-stage Vision Transformer (ViT) model with multi-dimensional signal representation，is then designed for the recognition and classification of jamming signals.Experimental results show that the proposed method achieves higher recognition accuracy and efficiency than the conventional methods，while maintaining robustness under low signal-to-noise ratios and diverse interference scenarios.These results demonstrate the method’s adaptability to complex environments and its potential to support rapid decision-making in modern battlefields.This study provides a feasible path for the engineering application of distributed communication jamming technologies and establishes a technical basis for the development of air-sea integrated electronic countermeasure systems.

Key words: distributed communication jamming, air-sea integrated architecture, signal recognition, vision transformer

王珣, 吴志强, 刘明骞. 基于ViT网络模型自注意力机制的干扰类型分布式识别方法[J]. 兵工学报, 2025, 46(S1): 250127-.

WANG Xun, WU Zhiqiang, LIU Mingqian. A Distributed Recognition Method for Interference Types based on the Self-attention Mechanism of ViT Network Model[J]. Acta Armamentarii, 2025, 46(S1): 250127-.

图/表 13

图1 时频表征对比

Fig.1 Comparison chart of time-frequency representation

图2 通信干扰信号的时频图

Fig.2 Time-frequency diagram of communication jamming signal

图3 通信干扰信号的星座图

Fig.3 Constellation Diagram of Communication Interference Signal

图4 ViT网络模型结构

Fig.4 Structure of ViT network model

图5 Encoder Block结构

Fig.5 Encoder Block structure

图6 ViT网络模型采用的自注意力机制

Fig.6 Self-attention mechanism used in the ViT network model

图7 Transformer Encoder层及FFN结构

Fig.7 Transformer Encoder layer and feedforward neural network structure

图8 基于ViT网络模型自注意力机制的干扰类型识别流程图

Fig.8 Flowchart for interference type recognition based on the self-attention mechanism of ViT network model

图9 基于ViT网络模型的干扰类型识别性能

Fig.9 Interference Type Recognition Performance Based on the ViT Network Model

图10 不同JNR下的混淆矩阵性能

Fig.10 Performance Comparison of Confusion Matrices under Different JNRs

图11 不同频率偏移下通信干扰的识别性能

Fig.11 Recognition oerformance of communication interference under different frequency offsets

图12 不同信道模型下信号平均识别率的性能

Fig.12 The performance of average signal recognition rate under different channel models

图13 不同通信干扰识别方法的性能

Fig.13 The performance of different communication interference identification methods

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