Cross-receiver specific emitter identification based on a deep adversarial neural network with separated batch normalization

Specific Emitter Identification (SEI) is the approach to identify emitter individuals using received wireless signals. Despite the fact that deep learning has been successfully applied in SEI, the performance is still unsatisfying when the receiver changes. In this paper, we introduce a domain adaptation method, namely Deep Adversarial Neural Network (DANN), for cross-receiver SEI. Furthermore, separated batch normalization (SepBN) is proposed to improve the performance. Results of experiments using real data show that the proposed SepBN-DANN method performs desirably for cross-receiver SEI.