基于持续学习和联合特征提取的特定辐射源识别  

Specific Emitter Identification Based on Continuous Learning and Joint Feature Extraction

作　　者：张立民 谭凯文 闫文君 张婷婷 汤淼 ZHANG Limin;TAN Kaiwen;YAN Wenjun;ZHANG Tingting;TANG Miao(Institute of Information Fusion,Naval Aviation University,Yantai 264001,China)

机构地区：[1]海军航空大学信息融合研究所,烟台264001

出　　处：《电子与信息学报》2023年第1期308-316,共9页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(91538201);泰山学者工程专项经费基金(ts201511020)。

摘　　要：针对特定辐射源识别(SEI)识别准确率较低和单次样本学习花销较大的问题,该文提出一种基于增量式学习的SEI方法,设计多个连续增量深度极限学习机(CIDELM)。从截获信号中分别提取变分模态分解(VMD)后的Hilbert谱投影和高阶谱,降维后作为射频指纹(RFF)用于分类;在极限学习机(ELM)中采用稀疏自编码结构对多个隐含层进行无监督训练,并利用参数搜索策略确定最佳隐含层数和隐节点个数,实现对多批次标记样本的连续在线匹配。实验结果表明,该方法对不同调制方式、载波频率和收发距离均能表现出良好兼容性,能够实现对于多个辐射源个体的有效识别。Considering the problem of low recognition accuracy of Specific Emitter Identification(SEI) and high cost of single training, an SEI scheme based on incremental learning is proposed in this paper, multiple Continuous Incremental Deep Extreme Learning Machine(CIDELM) are designed. The Hilbert spectrum projection and higher-order spectrum processed by Variational Mode Decomposition(VMD) are extracted from the original signal, and they are used as the Radio Fingerprint Feature(RFF) for classification after dimensionality reduction. In the Extreme Learning Machine(ELM), the sparse self-encoding structure is introduced to perform unsupervised training on multiple hidden layers, and the parameter search strategy is used to determine the best number of hidden layers and hidden nodes, realizing online multi-batch labeled samples continuous matching. The results show that the algorithm can show good compatibility with different modulation modes, carrier frequencies and transmission distances, and can effectively identify multiple transmitters.

关 键 词：特定辐射源识别 增量学习 变分模态分解 高阶谱 深度极限学习机 

分 类 号：TN911.7[电子电信—通信与信息系统]