SAR有源干扰分类识别图像数据集仿真构建

doi:10.12305/j.issn.1001-506X.2025.10.13

Abstract

Abstract:

Synthetic aperture radar （SAR） images can provide rich scene situational information, and with the gradually deteriorating electromagnetic environment much attention has been attracted to SAR anti-interference techniques. Deep learning technologies have shown excellent performance in image classification and recognition in recent years. Considering the large demand for active interference SAR image data for training deep models and the situation of lack of interference image datasets, based on the analysis of 13 typical active interference patterns and their interference characteristics against SAR, a simulation-based SAR interference image dataset is constructed from the perspective of multi-scenario interference parameter coverage, on which the interference classification and identification research is carried out, with a constructed Transformer-based interference recognition model and multiple typical models. Experimental results show that, due to the introduction of a global self attention mechanism combined with a deformable attention mechanism, the proposed Transformer-based interference recognition model has stronger global modeling ability of interference features and identification ability of interference regions, and exhibits berrer performance compared to other models on the constructed dataset.

Key words: synthetic aperture radar （SAR） image, interference, classification and recognition, deep learning, dataset construction

CLC Number:

TP 18

Fei GAO, Ruida XI, Xiangwei XING, Yan XING, Qiang ZHANG, Hongxing DANG. Image dataset simulation constructing for SAR active interference classification and recognition[J]. Systems Engineering and Electronics, 2025, 47(10): 3257-3269.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Table 1

Value description of main interference parameters"

干扰样式	干扰参数	取值说明
射频噪声、噪声调幅、噪声调频和噪声调相	输入干信比	$ 8.4\sim 23.4\;{\text{dB}} $均匀分布产生
	调制噪声带宽	$ 0.1\;{B_r} \sim {B_r} $均匀分布产生
	干扰机位置	场景中心方位$ \pm 20\;{\text{km}} $和距离$ \pm 180\;{\text{km}} $范围均匀分布产生
	调幅斜率$ {K_{{\text{AM}}}} $	$ 0.1 \sim 5 $均匀分布产生
	调频斜率$ {K_{{\text{FM}}}} $	$ {10^5} \sim 2 \times {10^7} $均匀分布产生
	调相斜率$ {K_{{\text{PM}}}} $	$ 0.1 \sim 100 $均匀分布产生
扫频噪声和梳状谱噪声	输入干信比	$ 8.4\sim 23.4\;{\text{dB}} $均匀分布产生
	调制噪声带宽	$ 0.1\;{B_r} \sim {B_r} $均匀分布产生
	干扰机位置	场景中心方位$ \pm 20\; {\text{km}} $和距离$ \pm 180\;{\text{km}} $范围均匀分布产生
	扫频范围$ {F_{\min }} $与$ {F_{\max }} $	$ {F_{\min }} = 0 $，$ {F_{\max }} $在$ {B_r}/3 \sim {B_r} $均匀分布产生
	扫频周期$ T $	$ 0.1\sim 20 \;{\text{μs}} $均匀分布产生
	调频斜率$ {K_{{\text{FM}}}} $	$ {10^5} \sim 2 \times {10^7} $均匀分布产生
	梳状谱噪声信号分量数	$ 3 \sim 10 $均匀分布产生
	梳状谱噪声分量幅度$ {a_i} $	$ 0.5 \sim 1.5 $均匀分布独立产生
	梳状谱噪声分量基带频点$ {f_{{\text{j,}}i}} $	$ - 0.5 \;{B_r} \sim 0.5\;{B_r} $均匀分布独立产生
	梳状谱噪声分量调频斜率$ {K_{{\text{FM,}}i}} $	$ 5 \times {10^6} \sim 50 \times {10^6} $均匀分布独立产生
脉冲卷积和噪声卷积	输入干信比	$ 8.4 \sim 23.4 \; {\text{dB}} $均匀分布产生
	调制噪声带宽	$ 0.1 \; {B_r} \sim {B_r} $均匀分布产生
	时延个数$ P $	$ 5 \sim 20 $均匀分布产生
	幅度$ {\alpha _p} $	1
	时延$ \Delta {t_p} $	场景方位中心$ \pm 3.3 \; {\text{μs}} $范围均匀分布独立产生
	噪声时长	$ 0.4 \sim 10.4 \; {\text{μs}} $均匀分布产生
间歇转发、切片转发、频谱弥散转发和移频	输入干信比	$ 8.4 \sim 23.4 \; {\text{dB}} $均匀分布产生
	干扰机位置	场景中心方位$ \pm 300 \; {\text{m}} $和距离$ \pm 300 \; {\text{m}} $范围均匀分布产生
	间歇转发次数$ N $	$ 5 \sim 10 $均匀分布产生
	间歇转发干扰参数$ L $	$ 2 \sim 3 $均匀分布产生
	采样转发间歇时间$ \Delta t $	$ 0.3 \sim 2.5 \; {\text{μs}} $均匀分布产生
	切片转发分段数$ N $	$ 3 \sim 5 $均匀分布产生
	切片转发干扰参数$ L $	$ 3 \sim 5 $均匀分布产生
	频谱弥散转发次数$ N $	$ 5 \sim 10 $均匀分布产生
	频谱弥散转发间隔时间$ \Delta t $	$ 0.3 \sim 2.5 \; {\text{μs}} $均匀分布产生
	移频次数$ N $	$ 1 \sim 5 $均匀分布产生
	移频量$ \Delta {f_n} $	$ - {B_r} \sim {B_r} $均匀分布独立产生
虚拟假目标	输入干信比	$ 8.4 \sim 23.4 \; {\text{dB}} $均匀分布产生
	干扰机位置	场景中心方位$ \pm 300 \; {\text{m}} $和距离$ \pm 300 \; {\text{m}} $范围均匀分布产生
	虚拟假目标/散射点数$ {N_c} $	$ 1 \sim 10 $均匀分布产生
	虚拟假目标/散射点位置	场景中心方位和距离均在$ \pm 0.5 $$ \sim $$ \pm 1 \; {\text{km}} $范围均匀分布独立产生

Table 1

Fig.4

Fig.5

Fig.6

Fig.7

Table 2

Table 3

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模型	指标	#1	#2	#3	#4	#5	#6	#7
AlexNet	P	90.5	95.4	58.5	50.0	54.7	60.0	63.9
AlexNet	R	69.1	85.6	71.1	68.0	66.0	43.3	73.2
VGG16	P	87.0	97.8	76.1	63.3	64.5	67.7	77.8
VGG16	R	89.7	89.7	72.1	76.3	71.1	47.4	93.9
ResNet18	P	88.8	91.0	73.8	65.1	73.7	67.5	90.0
ResNet18	R	89.7	93.9	81.4	71.1	72.2	53.6	92.8
ResNet50	P	84.0	97.8	72.3	66.3	77.0	55.2	87.3
ResNet50	R	91.8	91.8	75.3	67.0	58.8	66.0	91.8
ResNet101	P	88.0	87.5	75.5	66.1	79.2	63.3	90.5
ResNet101	R	90.7	93.8	76.3	74.2	62.9	63.9	88.7
ViT-B/16	P	93.6	96.0	77.6	74.0	82.4	69.7	93.8
ViT-B/16	R	90.7	96.9	85.6	76.3	72.2	71.1	92.8
本文模型	P	97.9	97.9	76.0	80.9	81.4	73.7	93.9
本文模型	R	95.9	97.9	74.8	74.2	72.2	72.2	95.9

模型	指标	#8	#9	#10	#11	#12	#13	平均值	Acc
AlexNet	P	85.1	100.0	98.9	96.9	100.0	100.0	81.0	@1: 79.1
AlexNet	R	76.3	90.7	87.7	96.9	100.0	100.0	79.0	@5: 96.5
VGG16	P	93.5	100.0	97.9	100.0	100.0	100.0	86.6	@1: 86.2
VGG16	R	88.7	98.0	95.9	98.0	100.0	100.0	86.2	@5: 99.2
ResNet18	P	91.8	100.0	100.0	97.9	100.0	100.0	87.7	@1: 87.7
ResNet18	R	91.2	95.9	99.0	99.0	100.0	100.0	87.7	@5: 99.5
ResNet50	P	92.7	100.0	100.0	97.9	100.0	100.0	87.0	@1: 86.4
ResNet50	R	91.8	92.8	99.0	97.9	100.0	100.0	86.4	@5: 99.4
ResNet101	P	92.7	100.0	100.0	97.0	98.9	98.9	87.5	@1: 87.4
ResNet101	R	91.8	96.9	97.9	98.9	100.0	100.0	87.4	@5: 99.6
ViT-B/16	P	92.9	99.0	100.0	99.0	100.0	100.0	90.6	@1: 90.5
ViT-B/16	R	94.9	98.9	98.9	98.9	100.0	98.9	90.5	@5: 99.7
本文模型	P	96.8	100.0	100.0	100.0	100.0	100.0	92.2	@1: 92.1
本文模型	R	73.8	100.0	100.0	100.0	100.0	100.0	92.1	@5: 100.0

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Image dataset simulation constructing for SAR active interference classification and recognition

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