Yuxuan He, Kunda Wang, Qicheng Song, Huixin Li, Bozhi Zhang
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引用次数: 0
Abstract
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.