Convolutional Mixed Multi-Attention Encoder-Decoder Network for Radar Signal Sorting
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摘要: 雷达信号分选是电磁环境感知领域中的关键技术之一。随着雷达辐射源调制样式、工作模式、协同方式日益复杂，对雷达辐射源的侦收过程中，虚假脉冲、脉冲丢失、参数测量误差等问题日益突出，常规信号分选方法性能严重下降。针对上述问题，该文提出了一种卷积混合多注意力的编解码网络，其中编码器与解码器均基于双分支扩张瓶颈模块构建，通过并行膨胀卷积路径捕获多尺度时序模式，逐步扩大感受野以融合上下文信息；在编解码器间嵌入局部注意力模块，用于建模脉冲序列的时序依赖关系并增强全局表征能力；同时在跳跃连接中引入特征选择模块，自适应地筛选多阶段特征图中的关键信息，最终通过分类器实现逐脉冲的雷达信号分类，进而实现分选。仿真实验表明，与主流基线方法相比，在脉冲丢失和虚假脉冲概率高且存在脉冲到达时间估计误差等复杂条件下，所提出方法具有更好的信号分选性能。Abstract:
Objective Radar signal sorting is a fundamental technology in electromagnetic environment awareness and electronic warfare systems. The objective of this paper is to develop an effective radar signal sorting method capable of accurately separating intercepted pulse sequences and assigning them to different radiation sources under complex electromagnetic environments. With the increasing complexity of modern radar systems, intercepted pulse sequences are often severely affected by pulse overlap, pulse loss, false pulses, and pulse arrival time measurement errors, which significantly degrade the performance of conventional sorting approaches. Therefore, designing a robust signal sorting framework that can maintain high accuracy under such non-ideal conditions is of practical importance. Methods First of all, radar signal sorting in complex electromagnetic environments is formulated as a pulse-level time-series semantic segmentation problem, where each pulse is treated as the minimum processing unit and classified in an end-to-end manner. Under this formulation, sorting is achieved through unified sequence modeling and label prediction, without relying on explicit pulse subsequence extraction or iterative stripping procedures, thereby reducing error accumulation. To accomplish this task, a convolutional mixed multi-attention encoder–decoder network is presented ( Fig. 1 ), consisting of an encoder–decoder backbone, a local attention module, and a feature selection module. The encoder–decoder backbone adopts a symmetric structure with progressive downsampling and upsampling to aggregate contextual information while restoring pulse-level temporal resolution. Its core component is a dual-branch dilated bottleneck module (Fig. 2 ), in which a 1×1 temporal convolution is first applied for channel projection. Two parallel dilated convolution branches with different dilation rates are then employed to construct multi-scale receptive fields, enabling simultaneous modeling of short-term local variations and long-term modulation patterns across multiple pulses, thereby ensuring robust temporal representation under pulse time shifts and missing pulses. To enhance long-range dependency modeling beyond convolutional operations, a local Transformer module is introduced between the encoder and decoder. By applying local self-attention on temporally downsampled feature maps, temporal dependencies among pulses are effectively captured with reduced computational complexity, while the influence of false and missing pulses is suppressed during feature aggregation. In addition, a feature selection module is incorporated into skip connections to mitigate feature redundancy and interference (Fig. 3 ). Through hybrid attention along temporal and channel dimensions, multi-level features are adaptively filtered and fused, emphasizing discriminative information for radiation source identification. During training, focal loss is employed to alleviate class imbalance and improve the discrimination of difficult and boundary pulses.Results and Discussions Experimental results demonstrate that the proposed network is capable of achieving pulse-level fine-grained classification for radar signal sorting and significantly outperforms mainstream baseline methods across various complex scenarios. Compared with existing approaches, an average sorting accuracy improvement of more than 6% is achieved under moderate interference conditions.In Multifunctional Radar (MFR) overlapping scenarios, the proposed model achieves recall rates of 88.30%, 85.48%, 86.89%, and 86.48% for four different MFRs, respectively, with an overall average accuracy of 86.82%. For different PRI modulation types, recall rates exceed 90% for fixed patterns and remain above 85% for jittered, staggered, and group-varying modes. In staggered and group-varying cases, performance improvements exceeding 4% over baseline methods are observed. Generalization experiments further indicate that high accuracy is maintained under parameter distribution shifts of 5% and 15%, demonstrating strong robustness to distribution perturbations ( Fig. 8 ). Ablation studies confirm the effectiveness of each proposed module in improving overall performance.Conclusions A convolutional mixed multi-attention encoder–decoder network is presented for radar signal sorting in complex electromagnetic environments. By modeling radar signal sorting as a pulse-level time-series semantic segmentation task and integrating multi-scale dilated convolutions, local attention modeling, and adaptive feature selection, high sorting accuracy, robustness, and generalization capability are achieved under severe interference conditions. The experimental results demonstrate that the proposed approach provides an effective and practical solution for radar signal sorting in complex electromagnetic environments. -
表 1 雷达参数设置
雷达 RF/GHz PW/μs PRI/μs PRI调制方式 1 8.1～8.5 20～40 540 固定 2 8.15～8.3 30～45 370, J=0.25 抖动 3 8.1～8.45 15～40 110, 280, 800 参差 4 8.25～8.6 25～45 400·20, 600·15, 900·10 组变 
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表 2 MFR参数设置
雷达 RF/GHz PW/μs PRI/μs PRI调制方式 1 8.15～8.4 15～45 240 固定 8.2～8.45 20～50 310,J=0.1 抖动 8.25～8.5 25～40 370,410,260 参差 2 8.1～8.5 20～40 480,J=0.1 抖动 8.2～8.45 30～50 190,240,380 参差 8.15～8.4 25～45 290·20,330·14,210·10 组变 3 8.2～8.4 10～25 430,340 参差 8.45～8.8 15～40 230·10,330·8,180·24,280·12 组变 8.3～8.55 20～40 320 固定 4 8.1～8.45 25～45 410·15,350·20 组变 8.2～8.5 30～50 390 固定 8.15～8.6 20～40 150,J=0.2 抖动
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表 3 仿真实验结果(%)
类别 本文模型 TCAN DCN GRU-FCN Bi-GRU 总体准确率 92.58 86.24 84.22 83.81 73.38 雷达一 90.72 88.03 83.80 77.69 50.73 雷达二 91.65 80.92 85.66 85.81 76.40 雷达三 96.98 96.91 97.32 96.42 99.81 雷达四 89.72 77.08 63.28 69.21 55.32
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表 4 MFR仿真实验结果(%)
类别 本文模型 TCAN DCN GRU-FCN Bi-GRU 平均准确率 86.82 83.52 82.32 76.72 68.44 雷达一 88.30 83.78 84.16 82.99 75.01 雷达二 85.48 80.64 78.27 72.71 67.48 雷达三 86.89 82.54 82.59 72.86 60.74 雷达四 86.48 87.04 84.03 77.95 70.36
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表 5 各调制模式对比(%)
类别 本文模型 TCAN DCN GRU-FCN Bi-GRU 固定 90.47 87.77 86.33 79.56 78.67 抖动 85.11 85.22 82.40 77.22 72.37 参差 88.68 81.62 84.51 79.48 66.79 组变 85.46 81.90 78.37 72.79 57.78
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表 6 模型计算开销对比
算法模型 本文模型 TCAN DCN GRU-FCN 参数量 401.68K 329.50K 158.17K 38.15K FLOPs 103.88M 165.40M 80.50M 20.02M 存储空间 1.53MB 1.26MB 0.60MB 0.15MB
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表 7 消融实验结果(%)
模型 场景一 场景二 场景三 Baseline 81.4 76.2 72.0 Baseline+A 86.1 82.6 75.9 Baseline+A+B 88.2 84.3 76.2 Baseline+A+B+C 92.6 87.4 80.4
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