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摘要: 为提高雷达在复杂电磁环境下的干扰识别能力，该文提出一种具备智能化、轻量化以及高实时性3项主要特性的轻量化复合干扰信号识别网络YOLO-S3。首先，提出利用视觉检测算法对雷达干扰的二维时频数据进行识别的技术路线，并基于信号建模仿真技术和短时傅里叶变换构建复合干扰信号图像数据集。其次，通过引入StarNet和SlimNeck重构YOLOv8n的主干及颈部网络，同时设计具有自注意力机制的检测头(SADH)，在保证识别精度的同时实现了网络轻量化。最后，通过消融实验和对比实验验证了网络性能。实验结果表明，YOLO-S3具有最轻量化的网络设计。在信干比–10～0 dB范围内随机分布的条件下，当信噪比≥0 dB时，该网络的平均识别精度高达99.5%；当信噪比降至–10 dB时，仍可保持95.5%的平均识别精度，在低信噪比条件下表现出较好的鲁棒性和泛化能力。该文的研究成果为机载雷达信号处理器、便携式电子设备等资源受限平台的实时复合干扰信号识别提供了新的技术途径。Abstract: To enhance the jamming recognition capabilities of radars in complex electromagnetic environments, this study proposes YOLO-S3, a lightweight network for recognizing composite jamming signals. YOLO-S3 is characterized by three core attributes: Smartness, slimness, and high speed. Initially, a technical approach based on visual detection algorithms is introduced to identify 2D time-frequency representations of jamming signals. An image dataset of composite jamming signals is constructed using signal modeling, simulation technology, and the short-time Fourier transform. Next, the backbone and neck networks of YOLOv8n are restructured by integrating StarNet and SlimNeck, and a Self-Attention Detect Head (SADH) is designed to enhance feature extraction. These modifications result in a lightweight network without compromising recognition accuracy. Finally, the network’s performance is validated through ablation and comparative experiments. Results show that YOLO-S3 features a highly lightweight network design. When the signal-to-jamming ratio varies from −10 to 0 dB and the Signal-to-Noise Ratio (SNR) is ≥0 dB, the network achieves an impressive average recognition accuracy of 99.5%. Even when the SNR decreases to −10 dB, it maintains a robust average recognition accuracy of 95.5%, exhibiting strong performance under low SNR conditions. These findings provide a promising solution for the real-time recognition of composite jamming signals on resource-constrained platforms such as airborne radar signal processors and portable electronic devices.
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图 3 JSID19200数据集各类型干扰分布情况
Figure 3. Composition of jamming signal types in the JSID19200 dataset
图 12 不同SNR下识别情况的混淆矩阵
Figure 12. Confusion matrix of signal recognition under varying SNR conditions
表 1 信号仿真参数
Table 1. Signal simulation parameters
信号类型 参数 取值范围 LFM 起始频率 –10 MHz 终止频率 10 MHz 单信号总样本数 4000 点有效调制段样本数 [600, 800, 1000 ]点采样率 40 MHz SNR 0～10 dB LFM+DFTJ 假目标数量 [3, 4]个 假目标时延 [5, 7, 9] μs 对应样本数 [200, 280, 360]点 SNR 0～10 dB SJR –10～0 dB LFM+ISRJ 转发次数 [3, 4]个 采样时间 [5, 6, 8] μs 对应样本数 [200, 240, 320]点 SNR 0～10 dB SJR –10～0 dB LFM+NPJ SNR 0～10 dB SJR –10～0 dB LFM+DFTJ+ISRJ
LFM+DFTJ+NPJ
LFM+ISRJ+NPJ
LFM+DFTJ+ISRJ+NPJ假目标数量 [3, 4]个 假目标时延 [5, 7, 9] μs 转发次数 [3, 4]个 采样时间 [5, 6, 8] μs SNR 0～10 dB SJR –10～0 dB 
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表 2 JSID19200数据集详细信息
Table 2. JSID19200 dataset specifications
信号类型 训练集+验证集数量 测试集数量 LFM 1200 1200 LFM+DFTJ 1200 1200 LFM+ISRJ 1200 1200 LFM+NPJ 1200 1200 LFM+DFTJ+ISRJ 1200 1200 LFM+DFTJ+NPJ 1200 1200 LFM+ISRJ+NPJ 1200 1200 LFM+DFTJ+ISRJ+NPJ 1200 1200 总计 9600 9600
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表 3 实验环境与参数配置
Table 3. Experimental environment and parameter configuration
环境 配置 参数 数值 操作系统 Windows 10 图像尺寸 640 CPU AMD EPYC 7H12 64-Core Processor 批量大小 16 GPU NVIDIA RTX A6000 迭代次数 100 显存 48 GB 优化器 SGD Python 3.9.2 初始学习率 0.01 Pytorch 1.12.0 最终学习率 0.0001 CUDA 11.3 学习率动量 0.937 编译软件 PyCharm 2020.1.3 权重衰减系数 0.0005
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表 4 消融实验结果
Table 4. Ablation study results
StarNet SlimNeck SADH Params
(M)FLOPs
(GB)Size
(MB)Dtotal
(ms)P
(%)R
(%)AP(%) mAP
(%)LFM DFTJ ISRJ NPJ -- -- -- 3.0 8.2 6.3 46.4 99.8 99.7 99.5 99.5 99.5 99.5 99.5 √ -- -- 1.9 5.3 4.0 30.4 99.7 99.7 99.5 99.5 99.5 99.5 99.5 √ √ -- 2.0 4.2 4.2 34.7 99.8 99.7 99.5 99.5 99.5 99.5 99.5 √ √ √ 1.8 3.3 4.0 39.8 99.7 99.7 99.5 99.5 99.5 99.5 99.5
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表 5 常见网络对比实验结果
Table 5. Comparative study results of state-of-the-art networks
网络名称 Params
(M)FLOPs
(GB)Size
(MB)Dtotal
(ms)P
(%)R
(%)AP(%) mAP
(%)LFM DFTJ ISRJ NPJ Faster RCNN[21] 41.4 216.5 315.1 2437.5 95.5 94.0 99.0 84.1 99.0 99.8 95.5 SSD[22] 13.4 15.2 103.1 312.5 93.6 82.4 98.9 79.0 97.9 98.8 93.6 RetinaNet[23] 32.3 215.0 245.7 1920.3 96.8 94.1 99.6 89.2 99.0 99.4 96.8 RT-DETRx[24] 67.3 232.3 129.0 255.9 96.6 98.1 99.4 99.0 99.2 99.4 99.3 YOLOv8n[25] 3.0 8.2 6.3 46.4 99.8 99.7 99.5 99.5 99.5 99.5 99.5 YOLOv8x 68.2 258.1 136.7 214.3 99.8 99.9 99.5 99.5 99.5 99.5 99.5 YOLOv9t[26] 2.0 7.9 4.6 63.7 99.8 99.8 99.5 99.5 99.5 99.5 99.5 YOLOv9e 58.1 192.7 117.3 679.3 99.8 99.8 99.5 99.5 99.5 99.5 99.5 YOLOv10n[27] 2.7 8.4 5.8 49.4 99.3 99.5 99.4 99.5 99.5 99.5 99.5 YOLOv10x 31.7 170.0 64.1 229.9 99.8 99.8 99.5 99.5 99.5 99.5 99.5 YOLOv11n[28] 2.6 6.4 5.5 49.9 99.8 99.7 99.5 99.5 99.5 99.5 99.5 YOLOv11x 56.9 195.5 114.4 248.2 99.8 99.9 99.5 99.5 99.5 99.5 99.5 YOLOv12n[29] 2.6 6.5 5.5 63.2 99.8 99.8 99.5 99.5 99.5 99.5 99.5 YOLOv12x 59.1 199.8 119.1 343.6 99.8 99.9 99.5 99.5 99.5 99.5 99.5 YOLO-S3 1.8 3.3 4.0 39.8 99.7 99.7 99.5 99.5 99.5 99.5 99.5
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