Specific Emitter Identification Based on Deep Adversarial Domain Adaptation

Abstract

To solve the problem that the neural network-based specific emitter identification (SEI) method is limited by the training scene and has poor generalization ability in the complex scene, an SEI method based on deep adversarial domain adaptation (DADA) is proposed. DADA integrates deep neural networks and adversarial methods into the domain adaptation problem of transfer learning. It trains the feature extractor by maximizing the domain discrimination loss and transfers the high-quality deep radio frequency fingerprint (RFF) features extracted under weak interference conditions to the unlabeled emitter of the same class affected by the channel and noise. At the same time, the loss of the sample classifier is minimized, and the deep RFF features of unlabeled emitters can be accurately classified and identified. By repeating the above-mentioned adversarial process, the network realizes unsupervised transfer learning based on deep RFF features. The analysis of the identification results of 20 CC2530 devices under the Rayleigh channel and different signal-to-noise ratio (SNR) conditions proves that DADA can effectively improve the network's identification performance for unlabeled emitters under different conditions and has good performance of adaptability and generalization.