基于深度CNN模型的SAR图像有源干扰类型识别方法

陈思伟; 崔兴超; 李铭典; 陶臣嵩; 李郝亮

doi:10.12000/JR22143

基于深度CNN模型的SAR图像有源干扰类型识别方法

DOI: 10.12000/JR22143

国防科技大学电子科学学院电子信息系统复杂电磁环境效应国家重点实验室长沙 410073

基金项目: 国家自然科学基金(62122091, 61771480)，湖南省自然科学基金(2020JJ2034)

详细信息

作者简介:
陈思伟，博士，教授，主要研究方向为极化雷达成像与目标识别、机器学习、电子对抗等

崔兴超，博士生，主要研究方向为极化雷达成像与目标识别

李铭典，博士生，主要研究方向为极化雷达成像与目标识别

陶臣嵩，博士生，主要研究方向为电子对抗、机器学习

李郝亮，博士生，主要研究方向为极化雷达成像与目标识别

通讯作者:
陈思伟 chenswnudt@163.com

责任主编：毕大平 Corresponding Editor: BI Daping
中图分类号: TN958
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出版历程
- 收稿日期: 2022-07-09
- 修回日期: 2022-09-19
- 网络出版日期: 2022-09-30
- 刊出日期: 2022-10-28

SAR Image Active Jamming Type Recognition Based on Deep CNN Model

The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (62122091, 61771480), The Natural Science Foundation of Hunan Province (2020JJ2034)

More Information

Corresponding author: CHEN Siwei, chenswnudt@163.com

摘要

摘要: 合成孔径雷达(SAR)能够全天时全天候获取感兴趣区域的高分辨率雷达图像，在诸多领域获得了成功应用。在电子对抗博弈环境下，SAR图像解译与情报生成也面临复杂电磁干扰的严重影响。当前，国内外学者提出了许多SAR抗干扰技术方法。然而，作为抗干扰的前提，SAR图像干扰类型识别这一关键技术却鲜有报道。该文针对SAR图像典型有源干扰类型识别开展研究。首先，选取5种典型有源干扰样式，并根据干扰参数，细分为9种干扰类型，作为干扰识别对象。其次，开展干扰信号回波仿真，通过与MiniSAR实测数据进行回波域叠加和成像处理，构建了典型有源干扰类型样本集。在此基础上，提出了一种结合注意力机制的深度卷积神经网络(CNN)模型，并开展了对比实验验证。实验表明，对不同场景和不同干扰参数情形，相比于传统深度CNN模型，该文方法取得了更高的识别精度和更稳健的性能。
- 合成孔径雷达 /
- 有源干扰 /
- 深度学习 /
- 注意力机制 /
- 识别
Abstract: Synthetic Aperture Radar (SAR) can acquire high-resolution radar images of region of interest under all-day and all-weather conditions, a capability that has been successfully applied in many fields. In the environment of military confrontation games, complex electromagnetic jamming severely impacts SAR image interpretation and intelligence generation. Scholars have proposed numerous SAR anti-jamming approaches to date. However, the recognition of SAR image jamming types, which is the prerequisite of anti-jamming, has rarely been reported. This work focuses on active jamming type recognition in SAR images. First, five typical active jamming modes are selected and further subdivided into nine jamming types based on various jamming parameters, which serve as the objects of jamming recognition. The typical active jamming datasets are then constructed based on the stacking of simulated jamming signal echoes and real-measured MiniSAR data in the echo domain and SAR imaging processing. Based on the jamming datasets, an attention-combining deep Convolutional Neural Network (CNN) model has been proposed. Thereafter, comparative experiments are performed. Experiments show that, compared with traditional deep CNN models, the proposed method achieves more accurate recognition and more stable performance across various scenes and jamming parameter configurations.
- Synthetic Aperture Radar (SAR) /
- Active jamming /
- Deep learning /
- Attention mechanism /
- Recognition

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图 1 干扰数据构建流程图

Figure 1. Flowchart of jamming data construction

下载: 全尺寸图片幻灯片

图 2 MiniSAR数据切片

Figure 2. Image chips of MiniSAR data

下载: 全尺寸图片幻灯片

图 3 包含9种干扰类型和9种干扰机位置的车辆目标SAR图像切片((a) 噪声压制干扰，(b) 固定移频干扰，(c) 随机移频干扰，(d) 步进移频干扰，(e) 分段移频干扰，(f) 距离向间歇采样转发干扰，(g) 方位向间歇采样转发干扰，(h) 微动调制干扰，(i) 延迟多抽头干扰)

Figure 3. SAR image chips with vehicle targets containing 9 jamming types and 9 jammer positions((a) Noise suppression jamming, (b) Fixed shift-frequency jamming, (c) Random shift-frequency jamming, (d) Stepped shift-frequency jamming, (e) Blocked shift-frequency jamming, (f) Intermittent sampling repeater jamming in range, (g) Intermittent sampling repeater jamming in azimuth, (h) Micro-motion modulation jamming, (i) Delayed multi-taps jamming)

下载: 全尺寸图片幻灯片

图 4 结合注意力机制的有源干扰类型识别网络模型

Figure 4. Active jamming type recognition network model based on attention mechanism

下载: 全尺寸图片幻灯片

图 5 实验1模型训练过程损失值曲线和平均总体识别精度

Figure 5. Loss value in model training process and mean overall accuracy of experiment 1

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图 6 实验2平均总体识别精度

Figure 6. Mean overall accuracy of experiment 2

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表 1 干扰参数设置

Table 1. Jamming parameter configuration

干扰类型	参数设置
噪声压制干扰	JSR=5.0, 7.5, 10.0, 12.5, 15.0, 17.5, 20.0, 22.5 dB
固定移频干扰	$R = 20$
随机移频干扰	$R = 20$, $a = - 1$, $b = 1$
步进移频干扰	$R = 20$, $ {f_{{{\text{d}}_{\text{0}}}}} = 0 $
分段移频干扰	$R = 20$, $N = 3$, $ {f_1} = {f_{\text{d}}} $, $ {f_2} = 0 $, $ {f_3} = - {f_{\text{d}}} $
距离向间歇采样转发干扰	$R = 20$, ${D_{\rm r}} = 0.1$
方位向间歇采样转发干扰	$R = 20$, ${D_{\rm r}} = 0.1$
微动调制干扰	$R = 20$, $L = 100$
延迟多抽头干扰	$R = 20$, $N = 2$

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表 2 实验1有源干扰类型识别结果(%)

Table 2. Active jamming type recognition results of experiment 1 (%)

测试集	方法	噪声干扰	固定移频	随机移频	分段移频	步进移频	距离向间歇采样	方位向间歇采样	延迟多抽头	微动干扰	OA
JSR_7.5	VGG-16	100±0	98.41±2.46	100±0	97.46±2.15	99.68±0.63	100±0	100±0	94.29±4.33	99.37±0.78	98.80±0.70
	ResNet-18	100±0	70.48±9.66	100±0	90.79±2.54	86.35±5.37	92.06±5.22	100±0	69.21±2.94	91.11±3.11	88.89±1.59
	Inception v4	90.79±9.64	85.08±3.84	100±0	93.65±6.43	97.46±1.27	75.56±7.88	99.68±0.63	87.62±4.64	94.60±2.94	91.60±0.56
	VGG-16+AN	100±0	99.68±0.63	100±0	99.05±0.78	100±0	100±0	100±0	99.37±0.78	100±0	99.79±0.13
JSR_12.5	VGG-16	100±0	99.15±0.98	100±0	98.31±1.93	99.89±0.21	100±0	100±0	95.56±3.61	99.47±0.67	99.15±0.63
	ResNet-18	100±0	75.87±8.75	100±0	93.86±2.45	90.69±3.82	96.40±2.38	100±0	73.12±4.06	93.97±3.20	91.55±1.32
	Inception v4	96.03±4.89	88.89±2.39	100±0	95.24±5.48	98.10±0.86	84.97±4.50	99.89±0.21	90.90±3.13	96.51±2.43	94.50±0.57
	VGG-16+AN	100±0	99.89±0.21	100±0	99.58±0.40	99.79±0.42	100±0	100±0	99.58±0.62	100±0	99.87±0.11
JSR_17.5	VGG-16	100±0	99.37±0.74	100±0	98.65±1.60	99.92±0.16	100±0	100±0	96.03±3.33	99.60±0.50	99.29±0.55
	ResNet-18	100±0	78.81±7.79	100±0	95.00±2.21	92.06±3.57	97.30±1.78	100±0	74.68±3.75	94.92±2.98	92.53±1.20
	Inception v4	97.02±3.67	90.48±2.20	100±0	95.79±5.16	98.49±0.77	87.62±4.03	99.92±0.16	91.75±3.14	97.14±2.11	95.36±0.63
	VGG-16+AN	100±0	99.92±0.16	100±0	99.68±0.30	99.84±0.32	100±0	100±0	99.44±0.59	100±0	99.88±0.10
JSR_22.5	VGG-16	100±0	99.49±0.59	100±0	98.92±1.28	99.94±0.13	99.24±0.25	100±0	96.44±3.08	99.68±0.40	99.30±0.51
	ResNet-18	100±0	81.33±6.96	100±0	95.94±1.84	93.52±3.07	96.51±2.10	99.94±0.13	76.13±3.86	95.81±2.51	93.24±1.01
	Inception v4	97.62±2.93	91.94±2.07	100±0	96.32±4.42	98.73±0.72	89.52±3.83	99.94±0.13	91.87±3.24	97.65±1.69	95.95±0.64
	VGG-16+AN	100±0	99.94±0.13	100±0	99.75±0.24	99.87±0.25	99.49±0.52	100±0	99.37±0.57	100±0	99.82±0.12

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表 3 实验2有源干扰类型识别结果(%)

Table 3. Active jamming type recognition results of experiment 2 (%)

测试集	方法	噪声干扰	固定移频	随机移频	分段移频	步进移频	距离向间歇采样	方位向间歇采样	延迟多抽头	微动干扰	OA
Para_20	VGG-16	100±0	99.37±0.74	100±0	98.49±1.38	99.60±0.25	99.84±0.19	100±0	96.67±2.95	99.68±0.30	99.29±0.48
	ResNet-18	99.86±0.28	77.94±7.19	100±0	93.89±2.47	91.11±3.30	94.37±2.51	100±0	72.86±3.31	93.65±2.50	91.52±1.27
	Inception v4	94.03±3.11	89.92±2.87	100±0	94.76±5.05	98.10±1.16	84.44±5.12	99.44±0.59	91.67±3.65	96.98±1.41	94.37±0.77
	VGG-16+AN	100±0	99.92±0.16	100±0	99.52±0.30	99.92±0.16	100±0	100±0	99.37±0.78	100±0	99.86±0.09
Para_22	VGG-16	100±0	98.89±0.95	100±0	93.10±5.64	99.84±0.19	99.76±0.32	100±0	87.30±5.42	82.38±7.44	95.70±1.41
	ResNet-18	99.94±0.12	72.86±6.93	99.76±0.48	86.67±5.85	89.37±3.82	92.14±3.97	99.76±0.32	62.46±8.97	65.00±5.47	85.33±2.26
	Inception v4	94.15±3.18	91.75±4.37	100±0	73.50±16.35	95.71±1.21	83.49±5.11	99.29±0.53	91.90±4.68	72.22±8.26	89.12±1.43
	VGG-16+AN	100±0	99.44±0.69	100±0	96.59±4.34	99.60±0.35	99.84±0.19	100±0	95.16±6.31	93.81±2.85	98.27±1.14
Para_24	VGG-16	100±0	99.37±0.48	100±0	94.29±4.17	100±0	99.84±0.32	100±0	91.19±4.06	8.73±4.46	88.16±0.53
	ResNet-18	99.98±0.04	72.54±7.96	99.84±0.32	88.57±5.84	89.37±4.85	94.05±3.47	100±0	60.95±5.30	15.16±2.61	80.05±2.31
	Inception v4	94.21±3.38	92.46±2.81	100±0	76.11±9.43	96.35±2.67	83.41±5.06	98.49±0.85	91.11±8.15	8.89±6.80	82.34±0.64
	VGG-16+AN	100±0	99.68±0.30	100.0±0	99.29±1.24	100±0	99.60±0.50	100±0	92.70±8.58	6.51±4.93	88.64±1.36
Para_26	VGG-16	100±0	99.37±0.89	100±0	95.71±4.74	99.44±0.78	99.68±0.39	100±0	69.60±16.78	4.76±6.06	85.40±2.01
	ResNet-18	99.88±0.24	66.59±8.44	99.84±0.32	88.02±4.01	85.56±8.16	89.44±4.07	100±0	44.21±6.53	12.62±3.28	76.24±2.34
	Inception v4	94.17±3.60	91.67±3.98	100±0	64.60±17.06	93.49±3.28	84.44±4.49	99.84±0.32	73.80±13.37	7.78±4.67	78.88±0.95
	VGG-16+AN	100±0	99.84±0.32	100±0	92.5±12.77	99.05±1.17	98.33±2.39	100±0	73.40±23.68	1.19±1.20	84.94±3.68
Para_28	VGG-16	100±0	99.92±0.16	100±0	85.5±16.14	98.73±1.38	98.73±1.16	100±0	33.20±19.38	17.50±13.83	81.53±3.88
	ResNet-18	99.88±0.24	71.1±10.00	99.76±0.32	81.03±5.15	84.05±7.81	84.44±6.63	100±0	38.41±3.30	30.08±4.91	76.54±1.47
	Inception v4	94.07±3.67	92.46±3.21	100±0	49.1±22.05	93.73±4.39	81.59±5.35	99.60±0.43	52.90±17.55	26.90±11.50	76.72±2.78
	VGG-16+AN	100±0	100±0	99.84±0.32	82.7±19.32	97.22±3.13	97.14±2.71	100±0	42.50±26.56	29.60±11.54	83.25±5.08
Para_30	VGG-16	100±0	99.21±0.75	100±0	73.3±26.03	96.75±4.36	94.76±6.04	99.92±0.16	20.30±16.48	31.20±17.88	79.51±5.44
	ResNet-18	99.82±0.36	59.50±12.56	99.84±0.32	75.32±7.83	73.70±13.23	76.35±6.43	99.68±0.63	37.54±5.86	36.35±5.60	73.13±2.42
	Inception v4	93.91±3.35	89.84±3.76	100±0	45.60±20.60	88.41±8.62	78.57±6.04	99.13±0.30	39.50±16.96	37.90±10.90	74.77±2.82
	VGG-16+AN	100±0	99.84±0.19	99.84±0.32	79.7±19.91	94.92±4.77	93.25±6.03	99.92±0.16	34.30±26.16	46.67±7.65	83.17±4.71

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