摘要： 针对雷达辐射源个体识别中单一连续脉冲模型难以兼顾整体时序信息与单脉冲细粒度特征、单脉冲模型缺乏全局动态信息，导致复杂电磁环境下识别性能受限的问题，本文提出一种双分支轻量融合识别方法。首先，通过连续脉冲切分将原始脉冲序列划分为连续脉冲序列与单脉冲两类数据，构建脉间序列分支与单脉冲分支对应的数据集，并分别训练连续序列模型和单脉冲模型，以提取脉间时序特征和细粒度脉内特征，实现两类信息的互补建模。随后，分别设计特征级融合与决策级融合两种策略：在特征级融合中引入门控机制，通过学习不同分支特征的重要性权重，对连续脉冲特征与单脉冲特征进行自适应加权并构建联合特征表示；在决策级融合中，基于两模型的概率输出采用软投票方式整合预测结果，以提高识别稳定性。为验证方法有效性，在实测雷达数据集上开展对比实验与消融实验。结果表明，两种融合策略均优于单一模型，其中决策级融合较单一连续脉冲模型识别准确率提升约8个百分点，较单一单脉冲模型提升约3个百分点；特征级融合在模型参数量较基准模型降低两个数量级的情况下仍取得最优识别性能。研究结果表明，所提方法在保证识别精度的同时具备良好的轻量化优势与工程应用潜力。

Abstract: To address the problem that, in radar emitter individual identification, a single continuous-pulse model cannot simultaneously capture global temporal information and fine-grained single-pulse features, while a single-pulse model lacks global dynamic information, thereby limiting recognition performance in complex electromagnetic environments, this paper proposes a dual-branch lightweight fusion recognition method. First, the original pulse sequence is segmented into two types of data, namely continuous pulse sequences and single pulses, through continuous pulse segmentation. Corresponding datasets are constructed for the inter-pulse sequence branch and the single-pulse branch, and a continuous-sequence model and a single-pulse model are trained separately to extract inter-pulse temporal features and fine-grained intra-pulse features, thus enabling complementary modeling of the two types of information. Subsequently, two fusion strategies, namely feature-level fusion and decision-level fusion, are designed. In the feature-level fusion strategy, a gating mechanism is introduced to learn the importance weights of features from different branches, so that continuous-pulse features and single-pulse features can be adaptively weighted to construct a joint feature representation. In the decision-level fusion strategy, the probability outputs of the two models are integrated by soft voting to improve recognition stability. To verify the effectiveness of the proposed method, comparative experiments and ablation studies are conducted on a measured radar dataset. The results show that both fusion strategies outperform the individual models. Specifically, decision-level fusion improves the recognition accuracy by approximately 8 percentage points over the single continuous-pulse model and by about 3 percentage points over the single single-pulse model. Moreover, feature-level fusion achieves the best recognition performance while reducing the number of model parameters by two orders of magnitude compared with the baseline model. The results demonstrate that the proposed method can maintain high recognition accuracy while also exhibiting favorable lightweight characteristics and strong potential for engineering applications.