FSUNet: A Radar Pulse Stream Sorting Method Based on Structured State Modeling

Radar emitter signal deinterleaving separates pulse sequences from individual emitters within overlapping pulse streams. This is a critical step in radar signal intelligence processing, as its performance directly affects the accuracy of subsequent tasks such as waveform recognition, behavior analysis, and threat assessment. Current methods typically use pulse description words as the primary features, assigning pulse attribution based on parameter differences among emitters. However, in complex electromagnetic environments, the reliability of pulse description word estimation is highly susceptible to noise, measurement errors, and waveform overlap. These issues reduce the feature separability of pulses from different emitters, thereby limiting further improvements in deinterleaving performance. To address these challenges, this study proposes FSUNet, an end-to-end pulse stream deinterleaving network based on structured state modeling. FSUNet directly accepts the pulse stream within an observation window as input and jointly models intrapulse local morphological features and interpulse temporal dependencies within a unified framework. This enables an end-to-end mapping from raw signals to deinterleaving results. Specifically, FSUNet first utilizes a multiscale adaptive offset mechanism to extract pulse boundaries and local transient features. It then introduces a structured state space module to recursively model cross-pulse contextual dependencies, enhancing long-range temporal representation and improving the consistency of deinterleaving results. Finally, skip connections are combined with an attention-based fusion mechanism to adaptively integrate local details with global state information. Experimental results show that FSUNet achieves high deinterleaving accuracy and robustness across various scenarios while maintaining computational efficiency.