AWD 2 AR: An Unsupervised Identification Framework for Specific Emitters in Diverse Cross-Domain Scenarios

The performance of specific emitter identification (SEI) techniques is often significantly degraded due to changes in the signal distribution of targets to be identified and the lack of labels in the data. To address the aforementioned issue, this letter proposes a SEI method based on adaptive wavelet decomposition and domain adversarial regularization (AWD2AR) for multiple cross-domain scenarios. Firstly, AWD2AR preprocesses all the received signals to obtain more separable feature representations. Subsequently, AWD2AR compels the target domain feature extractor to learn domain-invariant features. Meanwhile, a metric-based regularization term is utilized to ensure the correct matching of various classes within the domain, thereby enhancing the model’s performance on the target domain. Experimental results on different datasets demonstrate that AWD2AR outperforms the state-of-the-art algorithms in various cross-domain conditions. Furthermore, the rationality of AWD2AR has been validated through ablation experiment.