YOLO-S<sup>3</sup>：一种轻量化的雷达复合干扰识别网络

王佳祥; 孟进; 李伟; 朱轩; 薄钧天; 刘倩如; 吴灏; 张嘉毫

doi:10.12000/JR25080

YOLO-S³：一种轻量化的雷达复合干扰识别网络

DOI: 10.12000/JR25080 CSTR: 32380.14.JR25080

王佳祥¹,
孟进¹,
李伟¹,
朱轩²,
薄钧天¹,
刘倩如¹,
吴灏¹,
张嘉毫^1, ,

1.
海军工程大学电磁能技术全国重点实验室武汉 430033
2.
武汉大学电气与自动化学院武汉 430073

基金项目: 国家自然科学基金(52177012)，国家杰出青年科学基金(52025072)，海军工程大学自主研发项目(2025500080)

详细信息

作者简介:
王佳祥，硕士生，主要研究方向为雷达智能感知和深度学习

孟　进，博士，教授，主要研究方向为电源转换、通信的电磁兼容性和新型电磁干扰减少技术

李　伟，博士，副教授，主要研究方向为自适应信号处理、射频功率放大器设计以及宽带放大器行为建模和失真补偿

朱　轩，博士生，主要研究方向为计算机视觉、信号处理和通信干扰检测

薄钧天，博士生，主要研究方向为干扰消除、波形优化和雷达信号处理

刘倩如，硕士生，主要研究方向为信道特性对干扰对消性能影响和补偿

吴　灏，博士，副教授，主要研究方向为自适应信号处理、波形优化、智能雷达和机器学习

张嘉毫，博士，副教授，主要研究方向为电磁兼容性、干扰消除、天线、大规模MIMO和无线体域网

通讯作者:
张嘉毫 jiahao.z@hotmail.com

责任主编：全英汇 Corresponding Editor: QUAN Yinghui
中图分类号: TN958
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出版历程
- 收稿日期: 2025-04-29
- 修回日期: 2025-08-14

YOLO-S³: A Lightweight Network for Radar Composite Jamming Signal Recognition

1.
National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China
2.
School of Electrical Engineering and Automation, Wuhan University, Wuhan 430073, China

Funds: The National Science Foundation of China (52177012), the National Fund for Distinguished Young Scholars (52025072), the Independent Research and Development Program of NUE (2025500080)

More Information

Corresponding author: ZHANG Jiahao, jiahao.z@hotmail.com

摘要

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。
- 雷达复合干扰 /
- 轻量化 /
- YOLO-S³ /
- 低信噪比 /
- 干扰识别
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.
- Radar composite jamming /
- Lightweight /
- YOLO-S³ /
- Low Signal-to-Noise Ratio (SNR) /
- Jamming signal recognition

HTML全文

图 1 复合干扰信号识别技术路线

Figure 1. Technical roadmap for composite jamming signal recognition

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图 2 信号时频图像

Figure 2. Time-frequency representation of signals

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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

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图 4 YOLO-S³网络结构

Figure 4. YOLO-S³ network architecture

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图 5 StarNet网络结构

Figure 5. StarNet network architecture

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图 6 SlimNeck网络结构

Figure 6. SlimNeck network architecture

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图 7 GSConv网络结构

Figure 7. GSConv network architecture

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图 8 VoV-GSCSP网络结构

Figure 8. VoV-GSCSP network architecture

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图 9 SADH网络结构

Figure 9. SADH network architecture

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图 10 各类信号识别的混淆矩阵

Figure 10. Confusion matrix for multi-class signal recognition

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图 11 复合干扰信号识别结果

Figure 11. Composite jamming signal recognition results

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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

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图 13 不同SNR识别精度变化曲线

Figure 13. Recognition accuracy vs. SNR curves

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图 14 未知干扰信号识别结果

Figure 14. Recognition results for unseen jamming signals

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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
LFM+NPJ	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)	D_total (ms)	P (%)	R (%)	AP(%)				mAP (%)
StarNet	SlimNeck	SADH	Params (M)	FLOPs (GB)	Size (MB)	D_total (ms)	P (%)	R (%)	LFM	DFTJ	ISRJ	NPJ	mAP (%)
--	--	--	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)	D_total (ms)	P (%)	R (%)	AP(%)				mAP (%)
网络名称	Params (M)	FLOPs (GB)	Size (MB)	D_total (ms)	P (%)	R (%)	LFM	DFTJ	ISRJ	NPJ	mAP (%)
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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