海军航空大学学报2024，Vol.39Issue(2)：189-198,260,11.DOI:10.7682/j.issn.2097-1427.2024.02.001
样本标签污染条件下的雷达辐射源个体识别技术
Individual Identification Technology of Radar Radiation Sources Under Sample Label Pollution Conditions
摘要
Abstract
In response to the problem of reduced recognition accuracy in Specific Emitter Identification(SEI)due to wrong label in the dataset,a supervised and unsupervised fusion method for mislabel recognition and correction is pro-posed.Firstly,the unsupervised density peak clustering method is used to identify samples with label errors in the dataset,and then K-fold crossover experiments are used to predict and vote on these samples with abnormal labels,using the label with a large number of votes as the result of correcting incorrect labels.The cleaned data set is trained by convolutional neural network to obtain an ideal network model for emitter individual recognition,which ensures that the emitter individ-ual recognition network can still have a good recognition accuracy under the condition of sample pollution.The recogni-tion accuracy of the proposed method is improved by an average of 3.3%when the label error rate is less than 30%com-pared to the unprocessed dataset.When the label error rate is greater than 30%,the individual recognition accuracy can al-so reach around 90%,verifying that the proposed method can achieve good results in identifying and correcting incorrect labels.关键词
辐射源个体识别/错误标签/密度峰值聚类/K折交叉实验/卷积神经网络Key words
SEI/wrong label/density peak clustering/K-fold crossover experiment/convolutional neural network分类
信息技术与安全科学引用本文复制引用
段可欣,闫文君,凌青,王艳艳,王艺卉..样本标签污染条件下的雷达辐射源个体识别技术[J].海军航空大学学报,2024,39(2):189-198,260,11.基金项目
国家自然科学基金面上项目(62271499) （62271499）
电磁空间安全全国重点实验室开放基金 （）
