基于多任务学习和多域特征融合的雷达辐射源信号分选方法OA

Radar emitter signal sorting(RESS)plays a critical role on modern electronic warfare.To address the limitations of traditional sorting methods,such as low efficiency and limited accuracy in complex electromagnetic environments,as well as radar-communication spectrum sharing(RCSS)brought about by the development of 5G communication technologies,this paper proposes an innovative solution based on multi-task learning and multi-domain feature fusion.First,the proposed method constructs a multi-task learning(MTL)framework to jointly preprocess noisy radar-communication mixed signals,including signal cleansing,denoising,and time-frequency feature enhancement.Second,a feature fusion network is designed to extract multi-scale features from both the time-frequency image domain and the time-delay-Doppler domain.An improved iterative attention mechanism is employed to achieve cross-domain feature fusion,resulting in a high-dimensional feature representation with a strong discriminative power.Finally,a clustering model based on DeepCluster is used to accurately sort radar emitter signals.Experiments demonstrates that under low signal-to-noise ratio(SNR)condition of-6 dB,the proposed method effectively suppresses communication signal interference and achieves a sorting accuracy of 93.8%.Compared with existing approaches,the proposed solution demonstrates significant advantages in enhancing the robustness of signal preprocessing and maintaining high sorting accuracy,underscoring its strong potential for practical engineering applications.