Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature(I/Q)signals,challenges persist due to signal-type confusion and background noise interfere...Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature(I/Q)signals,challenges persist due to signal-type confusion and background noise interference.To address those limitations,this paper proposes a multi-channel contrast prediction coding and complex-valued residuals network(MCPC-MCVResNet)framework.This model employs contrast prediction techniques to directly extract discriminative features from electromagnetic signal sequences,effectively capturing both amplitude and phase information within I/Q data.A core innovation of this approach is the sphere space softmax(SS-softmax)loss,which optimizes intra-class clustering density of while establishing well-defined boundaries between distinct emitters.The SS-softmax mechanism significantly enhances the model's capacity to discern subtle variations among radiation emitters.Experimental results demonstrate superior identification accuracy,rapid convergence,and exceptional robustness in low signal-to-noise ratio environments.展开更多
基金supported by the National Natural Science Foundation of China(62201602)。
文摘Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature(I/Q)signals,challenges persist due to signal-type confusion and background noise interference.To address those limitations,this paper proposes a multi-channel contrast prediction coding and complex-valued residuals network(MCPC-MCVResNet)framework.This model employs contrast prediction techniques to directly extract discriminative features from electromagnetic signal sequences,effectively capturing both amplitude and phase information within I/Q data.A core innovation of this approach is the sphere space softmax(SS-softmax)loss,which optimizes intra-class clustering density of while establishing well-defined boundaries between distinct emitters.The SS-softmax mechanism significantly enhances the model's capacity to discern subtle variations among radiation emitters.Experimental results demonstrate superior identification accuracy,rapid convergence,and exceptional robustness in low signal-to-noise ratio environments.
