FTAN: Feature Transform and Alignment Network for cross-domain specific emitter identification

Abstract

Conventional deep learning-based specific emitter identification (SEI) methods are consistently constrained to domain-invariant assumption, leading to a decrease in recognition accuracy when the feature domain changes. To tackle this issue, we propose a novel unsupervised domain adaptation (UDA) framework named feature transform and alignment network (FTAN) for cross-domain SEI. In FTAN, we first apply a weight-shared network to extract the initial features of signals from all domains. Then, we introduce domain-specific modules to individually learn domain-invariant features, which can minimize the distribution discrepancies of source and target signals. Finally, the aligned domain-invariant features are utilized for identification. We evaluate the performance of FTAN on the various signal datasets. The experimental results demonstrate that FTAN significantly mitigates identification performance degradation in cross-domain scenarios and outperforms other state-of-the-art methods.