Radar emitter signal recognition method based on improved collaborative semi-supervised learning  

作　　者：JIN Tao ZHANG Xindong 

机构地区：[1]College of Electronic Science and Engineering,Jilin University,Changchun 130012,China

出　　处：《Journal of Systems Engineering and Electronics》2023年第5期1182-1190,共9页系统工程与电子技术（英文版）

摘　　要：Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recogni-tion.To solve this problem,an optimized cooperative semi-supervised learning radar emitter recognition method based on a small amount of labeled data is developed.First,a small amount of labeled data are randomly sampled by using the bootstrap method,loss functions for three common deep learning net-works are improved,the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification.Subsequently,the dataset obtained after sam-pling is adopted to train three improved networks so as to build the initial model.In addition,the unlabeled data are preliminarily screened through dynamic time warping(DTW)and then input into the initial model trained previously for judgment.If the judg-ment results of two or more networks are consistent,the unla-beled data are labeled and put into the labeled data set.Lastly,the three network models are input into the labeled dataset for training,and the final model is built.As revealed by the simula-tion results,the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.

关 键 词：emitter signal identification time series BOOTSTRAP semi supervised learning cross entropy function homogeniza-tion dynamic time warping(DTW) 

分 类 号：TN957.51[电子电信—信号与信息处理]