基于行为识别网络的SAR工作模式识别方法

doi:10.12305/j.issn.1001-506X.2026.04.12

Abstract

Abstract:

To address the issues of low recognition accuracy and difficulty in engineering implementation for spaceborne synthetic aperture radar （SAR） working mode identification, a method based on the dual attention network-dialated convolution-Transformer behavior network is proposed. It combines the advantages of the Transformer network in time series processing and dilated convolution in expanding the receptive field and reducing computational load, constructing a working mode recognition network with Transformer as the backbone to effectively capture the potential temporal characteristics in SAR data. Additionally, to further enhance the model’s sensitivity to key information, the dual attention network is introduced, significantly improving the model’s recognition accuracy. Simulation results demonstrate the effectiveness of the method, providing technical support for the functional identification of remote sensing satellites.

Key words: spaceborne synthetic aperture radar, operating mode identification, Transformer, dual attention network, dilated convolution

CLC Number:

Jijun HU, Wei HAN, Guoyu ZHANG, Yang LI, Jie YANG, Tian TIAN. SAR operating mode recognition method based on behavioral recognition network[J]. Systems Engineering and Electronics, 2026, 48(4): 1218-1229.

Figures/Tables 12

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References 30

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工作模式	载频/GHz	脉宽/μs	带宽/MHz	幅度/dB	重复间隔/μs
条带	5.4	24.3	130~190	−66~–48	202.6
聚束	5.4	25.05	270~330	−50	208.75
滑动聚束	5.4	23	10~70	−55~−48	190
扫描	5.4	23.5/23.25/23.1	10~70	−66~−49	196/193.8/192.3

参数名称	配置
训练样本数	1600
样本类别	4
优化方法	Adam
批大小	128
样本维度	[128,5]
学习率	5e-4
训练轮数	30

网络模块	Transformer	空洞卷积	DANet	Accuracy/%
1	√	×	×	79.47
2	√	×	√	86.02
3	√	√	×	94.87
4	√	√	√	99.12

指标类别	网络结构
指标类别	CNN	CGRU	Informer	所提网络
准确率/%	85.96	91.85	95.22	99.12
参数数量（×10⁶）	0.148	0.229	0.162	0.154
计算复杂度（×10⁶）	3.687	9.168	5.086	4.870

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SAR operating mode recognition method based on behavioral recognition network

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