Abstract:  Due to the influence of the transmission environment and the change in the working state of the radiation source equipment， the channel noise of the signal to be identified and the training signal will be different， which will lead to the decrease of the recognition accuracy of the trained model. In order to solve this problem， most studies use single-source unsupervised domain adaptation method to use labeled samples under specific noise for the learning of unlabeled samples under the target noise to be identified. On the one hand， the labeled data collected in the actual situation may come from multiple source domains. On the other hand， the target domain can usually be regarded as a combination of multiple source domains. In order to explore the specific emitter recognition method based on multi-source unsupervised domain adaptation， a multi-source unsupervised domain adaptation method based on prototype alignment and contrast learning is proposed， which fully learns and utilizes the semantic structure information in the domain. Firstly， a prototype alignment method of multiple source domains and target domains is used to learn the feature representation of multiple source domains and a new pseudo-label strategy is designed. Then， this paper designs a weighted intra-domain sample-to-prototype comparative learning method to increase intra-class compactness and inter-class distinguishability. The experimental results on public datasets show that the proposed method achieves the best results in tasks with target domains of 4 db and 8 db， and the accuracy rates are 94.1 % and 97.4 %， respectively， which are 2.4 and 1.2 percentage points higher than the existing methods， indicating the effectiveness of the proposed method.

Key words: specific emitter identification, multi-source unsupervised domain adaptation, single-source unsupervised domain adaptation, prototype, comparative learning