基于置信度差异与熵最小化的跨时间鲁棒射频指纹识别方法OA

In real-world wireless electromagnetic environments,radio frequency fingerprint(RFF)identification systems are inevitably affected by time-varying channel conditions and environmental changes,which lead to distribution mis-match between training and testing data.Such mismatch causes notable performance degradation across time-varying sce-narios,severely limiting the stability and generalization capability of RFF identification models in unknown environ-ments.To address this challenge,a cross-domain generalization-oriented test-time adaptation method for RFF identifica-tion was proposed.The proposed approach did not rely on labeled target-domain data and mitigated environment-induced domain shifts by adaptively updating the model during the testing phase.Firstly,an Inception-based RFF identification network was designed to enhance the robustness of fingerprint features under complex channel conditions by exploiting multi-scale feature representations.Secondly,considering the practical constraint that neither source-domain data nor target-domain labels were accessible during testing,a source-free and unsupervised test-time adaptation framework was developed,enabling the model to progressively adapt to the target-domain distribution.Experimental results on multiple public datasets demonstrated that the proposed method achieved superior identification performance compared with exist-ing approaches,while maintaining strong robustness and generalization capability in cross-time scenarios.These results validate the effectiveness of the proposed method for practical RFF identification deployment in complex environments.