Low labeling rate specific emitter identification algorithm based on confidence learning
In order to solve the problem of weak labeling of samples with only a small amount of labeled data,a false label correction method based on confidence learning is proposed to identify individual radiation sources.Firstly,by dynamically adjusting the in-class confidence,the generated false labels can be corrected in time.Secondly,the sample value is analyzed and a small number of key samples that affect the performance of the model are manually labeled.Then,by using the joint cross-entropy and center loss function,the dynamic pseudo-label confidence loss is superimposed,and the inter-class difference and intra-class difference are paid attention to,so as to maximize the intra-class aggregation and inter-class separation achieved by using the data feature information.Experimental results show that the proposed algorithm can achieve effective identification of individual radiation sources under various conditions,such as 5%,10%,20%,50%,100%,especially in the case of low proportion of labeled data,the advantages are obvious,and the recognition accuracy rate exceeds 70%and 80%respectively,effectively alleviating the shortage of limited labeled samples.The recognition effect of reasonable network depth,good balance between precision and speed is realized.
specific emitter identificationpseudo label correctionsample value analysisloss function of pseudo label
NETL
NSTL
万方数据
