基于有向图连通性的无监督辐射源个体识别方法
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Unsupervised Specific Emitter Identification Based on Directed Graph Connectivity
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    摘要:

    摘 要:辐射源个体识别是指利用接收电磁信号中的独特细微特征来区分发射设备的技术。深度学习由于其强大的特征提取能力,逐渐成为实现辐射源个体识别的主要手段。但在非合作场景中无法获取大量带标签的数据样本来训练神经网络,且待识别的辐射源个数未知。为此,本文提出了无需指定辐射源个数的基于有向图连通性的无监督辐射源个体识别方法。受层次聚类的启发,首先基于局部密度将射频指纹特征空间划分为多个子簇,并将特征向量之间的关系映射为有向图;然后,基于有向图的连通性,将多个子簇进行合并,得到最终的识别结果。实验结果表明,在低信噪比条件下,所提方法能准确进行辐射源个体识别,识别性能较基准算法提高了7.1%-53.1%。

    Abstract:

    Abstract: Specific emitter identification (SEI) refers to the technique of distinguishing emitters by utilizing unique and subtle features in received electromagnetic signals. Due to its powerful feature extraction ability, deep learning has gradually become the main means of implementing SEI. However, in non-cooperative scenarios, labeled samples generally cannot be obtained to train the neural network, and the number of emitters to be identified is unknown. Therefore, this paper proposes an unsupervised SEI method based on directed graph connectivity without specifying the number of emitters. Drawing inspiration from the idea of hierarchical clustering, the radio frequency fingerprinting feature space is first divided into multiple sub-clusters based on local density, and the relationship between feature vectors is mapped to a directed graph. Then, based on the connectivity of the directed graph, the multiple subclusters are automatically merged to obtain the final identification result. The experimental results show that under low signal-to-noise ratio conditions, the proposed method can accurately identify individual emitters, and its identification performance is improved by 7.1%-53.1% compared to the benchmark algorithms.

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杨宁, 王桁,张邦宁,丁国如,郭道省.基于有向图连通性的无监督辐射源个体识别方法[J].数据采集与处理,,():

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  • 在线发布日期: 2025-05-22