Few-Shot Unsupervised Specific Emitter Identification Based on Density Peak Clustering Algorithm and Meta-Learning

Abstract

Deep-learning methods are becoming increasingly popular for improving the efficiency of specific emitter identification (SEI). However, in noncooperative scenarios, it is difficult to both obtain a large number of signal samples for the training of deep-learning models and assign labels to the acquired samples. To solve these two problems, a few-shot unsupervised SEI method is proposed in this study. First, the original radio-frequency (RF) signal is preprocessed based on the Hilbert–Huang transform (HHT) to obtain the Hilbert time–frequency spectrum, which can highlight RF fingerprints (RFFs) that can be used as signal training samples. Then, the Hilbert time–frequency spectrum is input to an improved autoencoder network for training to obtain the latent vector that can represent hidden features of the received signal. Next, the clustering by fast search and find of a density peaks [i.e., density peak clustering (DPC)] algorithm is used to cluster and label the latent vector, which is later reconstructed by the improved autoencoder network to obtain training samples with labeled information. Finally, the meta-learning algorithm is used to train the few-shot SEI network under the few-shot condition, so that it can distinguish different types of RF signals, corresponding to specific emitters. The experimental results show that the unlabeled signal training samples can be clustered and labeled accurately with the proposed method to perform SEI efficiently in few-shot scenarios. In comparison with state-of-the-art solutions, the proposed method in this study exhibited its superiority with regard to few-shot unsupervised SEI.