Individual Identification Technology of Radar Radiation Sources Under Sample Label Pollution Conditions
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.
国家科技期刊平台
NSTL
万方数据
维普
