电子测量与仪器学报

许敏,李博涵,王凯,谭守标.特定辐射源个体识别算法研究[J].电子测量与仪器学报,2021,35(10):116-123

特定辐射源个体识别算法研究

Research on individual identification method of specific emitter

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基金项目:国家自然科学基金（61772032）项目资助

作者	单位
许敏	1.安徽大学电子信息工程学院
李博涵	1.安徽大学电子信息工程学院
王凯	1.安徽大学电子信息工程学院
谭守标	1.安徽大学电子信息工程学院

Author	Institution
Xu Min	1.School of Electronics and Information Engineering, Anhui University
Li Bohan	1.School of Electronics and Information Engineering, Anhui University
Wang Kai	1.School of Electronics and Information Engineering, Anhui University
Tan Shoubiao	1.School of Electronics and Information Engineering, Anhui University

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中文摘要:

针对特定辐射源个体识别(specific emitter identification,SEI)方法,指纹特征提取需要复杂公式演算推理,特征差异小、提取困难,提取后特定辐射源个体识别正确率低的问题,提出一种基于密集连接结构与注意力机制的特定辐射源识别算法,称之为特定辐射源识别网络(specific emitter identification network,SEIN)。首先使用包络提取算法提取含噪声较少的辐射源信号包络,得到含有丰富指纹特征的包络图,进而进行 SEIN 指纹特征的提取及个体识别。实验结果表明,SEIN 可达到 95. 12%的分类识别效果,具有准确率高、指纹特征提取自动化特点,最终较好实现了复杂环境下特定辐射源个体识别。

英文摘要:

For the specific emitter identification method, fingerprint feature extraction needs complex formula calculus reasoning, the feature difference is small, the extraction is difficult, and the accuracy of specific emitter identification after extraction is low. In order to better extract fingerprint features, a specific emitter identification algorithm based on dense connection structure and attention mechanism is proposed, which is called specific emitter identification network ( SEIN). First, the envelope extraction algorithm is used to extract the envelope of the radiation source signal with less noise, and an envelope map with rich fingerprint features is obtained, then the SEIN fingerprint feature extraction and individual recognition are performed. The experimental results show that SEIN can achieve a classification and recognition effect of 95. 12%, has the characteristics of high accuracy and automatic fingerprint feature extraction, and finally achieves better specific emitter identification in complex environments.

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