Receiver-Agnostic Specific Emitter Identification via Latent Distribution Mixing in Frequency Domain

Specific emitter identification (SEI) is a crucial technique for recognizing individual emitters based on the unique characteristics of their radio frequency signals. Deep learning (DL)-based SEI has become the dominant method for identifying and authenticating wireless devices. However, in real-world applications, electromagnetic signals are subject to dynamic channel conditions and variations across different receivers, leading to significant performance degradation when models trained on specific datasets are applied to new, unseen environments. This variability challenges traditional DL methods, making domain generalization (DG) an essential approach to tackle this issue. In this paper, we propose a robust SEI method, latent distribution mixing (LDM), to enhance model generalization in receiver-agnostic scenarios. Our approach first applies the Fourier transform to convert time-domain signals into frequency-domain representations. Then, it mixes latent feature distributions across domains in the feature space to improve robustness, enabling the model to adapt to domain shifts effectively. We evaluated our method on a cross-receiver dataset, achieving a peak performance of 88.52%, surpassing other domain generalization methods. The experimental results demonstrate that the proposed LDM method offers a promising solution for SEI tasks in cross-receiver scenarios. Our code can be downloaded from https://github.com/frownean/LDM.