Yuxuan He, Kunda Wang, Qicheng Song, Huixin Li, Bozhi Zhang
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引用次数: 0
摘要
特定发射器识别是雷达信号处理领域的一项挑战。其目的是提取信号的个体指纹特征。然而,早期的工作都是利用信号或时频图像设计的,严重依赖于手工创建的特征计算或高维特征空间中的复杂交互。本文介绍了基于多模态特征交互的新型架构--时频多模态特征融合网络。具体来说,我们设计了一个时频信号特征编码模块、一个 wvd 图像特征编码模块和一个多模态特征融合模块。此外,我们还提出了一种名为 FMM 的特征点过滤机制,用于信号嵌入。与最先进的主流识别方法相比,我们的算法表现出很高的性能。结果表明,我们的算法优于其他算法,在准确率、精确度、召回率和 F1 分数上都达到了最高水平,分别比第二名高出 9.3%、8.2%、9.2% 和 9%。值得注意的是,直观结果表明,所提出的方法与信号生成机制相一致,能有效捕捉雷达数据的独特指纹特征。本文为 SEI 任务的后续多模态研究建立了一个基础架构。
Specific Emitter Identification Algorithm Based on Time–Frequency Sequence Multimodal Feature Fusion Network
Specific emitter identification is a challenge in the field of radar signal processing. Its aims to extract individual fingerprint features of the signal. However, early works are all designed using either signal or time–frequency image and heavily rely on the calculation of hand-crafted features or complex interactions in high-dimensional feature space. This paper introduces the time–frequency multimodal feature fusion network, a novel architecture based on multimodal feature interaction. Specifically, we designed a time–frequency signal feature encoding module, a wvd image feature encoding module, and a multimodal feature fusion module. Additionally, we propose a feature point filtering mechanism named FMM for signal embedding. Our algorithm demonstrates high performance in comparison with the state-of-the-art mainstream identification methods. The results indicate that our algorithm outperforms others, achieving the highest accuracy, precision, recall, and F1-score, surpassing the second-best by 9.3%, 8.2%, 9.2%, and 9%. Notably, the visual results show that the proposed method aligns with the signal generation mechanism, effectively capturing the distinctive fingerprint features of radar data. This paper establishes a foundational architecture for the subsequent multimodal research in SEI tasks.
ElectronicsComputer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍:
Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.