电讯技术2017，Vol.57Issue(7)：784-788,5.DOI:10.3969/j.issn.1001-893x.2017.07.009
数据库样本缺失下的雷达辐射源识别
Radar Emitter Identification in Database Sample Missing Condition
李蒙 1朱卫纲2
作者信息
- 1. 装备学院研究生管理大队 北京 101416
- 2. 装备学院光电装备系 北京 101416
- 折叠
摘要
Abstract
Present radar emitter identification based on machine learning technology mostly assumes that training set and test set are same.When the radar database and the true distribution of the signals are biased,the traditional classification method is ineffective.Thus,the theory of transfer learning is introduced into the identification system,and a radar emitter signal identification method based on structural discovery and re-balancing is proposed.By means of database data and target data clustering analysis and resampling,the distribution is corrected and the new data is put to support vector machine(SVM) for training and identifying reconnaissance samples.The simulation results show that the classification performance of the support vector machine model in the new training sample set has been greatly improved.关键词
雷达辐射源识别/迁移学习/结构发现/再平衡/支持向量机Key words
radar emitter identification/transfer learning/structural discovery/re-balancing/support vector machine(SVM)分类
信息技术与安全科学引用本文复制引用
李蒙,朱卫纲..数据库样本缺失下的雷达辐射源识别[J].电讯技术,2017,57(7):784-788,5.
