GLS: A hybrid deep learning model for radar emitter signal sorting

Abstract

Radar emitter signal sorting is a pivotal aspect of radar reconnaissance signal processing. The increasing density of the electromagnetic environment in modern radar pulse streams, coupled with the growing complexity and variability of operational modes and signal forms, results in extremely limited reference data. Consequently, most existing sorting methods fall short of meeting the performance requirements of modern electronic warfare. To enhance sorting performance under conditions of limited samples and labeled data, this paper proposes a radar emitter signal sorting model based on ResGCN-BiLSTM-SE (GLS). Firstly, we propose a novel adaptive weighted adjacency matrix construction method that aggregates multi-scale information of local and global features. Based on this, for GLS networks, the graph convolutional network (ResGCN) is combined with the bidirectional long short-term memory (BiLSTM) network. The GCN is employed to extract attribute features from interleaved radar pulse sequences, while the BiLSTM is utilized to deeply capture the temporal dependence in interleaved pulse sequences after feature extraction. Finally, an improved squeeze-and-excitation (SE) module is applied to perform weighted fusion of critical channel information from both spatial and temporal features. Simulation results demonstrate that the proposed method not only achieves higher accuracy under small sample conditions compared to existing methods, but also exhibits strong robustness in challenging scenarios involving measurement errors, missing pulses, and spurious pulses.