基于CBAM-Swin-Transformer迁移学习的海上微动目标分类方法

doi:10.12305/j.issn.1001-506X.2025.04.12

Abstract

Abstract:

As an important means for maritime target detection and identification, radar requires fine-grained description and classification of the motion characteristics of maritime targets, which is a key technology. Deep learning-based convolutional network classification method, although model-independent, still struggle to adapt to the complex and diverse maritime environment and the variety of maritime targets, with limited generalization ability. Convolutional block attention module (CBAM) is integrated into the Swin-Transformer network, and based on transfer learning (TL) strategy, a maritime micromotion target classification method (TL-CBAM-Swin-Transformer) is proposed to consider both ship targets and low-altitude rotorcraft flight targets, thereby enhancing the model classification adaptability under various observation conditions. Firstly, a maritime micromotion target model is established, and based on three radar measurement data sets, micromotion time-frequency datasets of sea surface non-uniform translational motion, three-axis rotation, helicopters, and fixed rotor drones are constructed. Then, the TL-CBAM-Swin-Transformer network is designed, where CBAM extracts features from both the channel and spatial dimensions, enhancing its ability to extract multi-head attention information at small scales. Experimental data verification result shows that compared to Swin-Transformer, the classification accuracy of the proposed algorithm is improved by 3.43%. By using TL method, the proposed network is pre-trained on the ImageNet dataset and is transferred to Council for Scientific and Industrial Research (CSIR) micromotion targets with intelligent pixel processing (IPIX) radar micromotion targets as the source domain for pre-training, achieving a classification probability of 97.9%. With helicopter rotors as the source domain for pre-training, the proposed algorithm transfer to fixed rotorcraft drones, achieving a classification probability of 98.8%, which vadidates the strong generalization ability of the proposed algorithm.

Key words: radar target classification, maritime micromotion target, transfer learning (TL), Swin-Transformer network, attention mechanism, time-frequency analysis

CLC Number:

TN958.2

Xiaoyang HE, Xiaolong CHEN, Xiaolin DU, Ningyuan SU, Wang YUAN, Jian GUAN. Classification of maritime micromotion target based on transfer learning in CBAM-Swin-Transformer[J]. Systems Engineering and Electronics, 2025, 47(4): 1155-1167.

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数据集种类	雷达类型	数据类型	训练集/张	测试集/张	目标种类	信噪比/dB
IPIX	X波段IPIX雷达^[15]	实测海杂波+仿真目标	25 981	10 400	匀变速目标、变加速目标、微动Ⅰ、微动Ⅱ	[-30, 10]
CSIR	X波段CSIR雷达^[29]	实测数据	-	1 000	匀变速目标、变加速目标、微动Ⅰ、微动Ⅱ	-
直升机	X波段雷达^[27]	实测海杂波+仿真目标	1 200	1 040	单翼机、双翼机、多翼机	[-20, 10]
无人机	Ku波段雷达^[29]	实测数据	-	125	固定翼无人机	[-5, 5]

运动类型	初速度/(m/s)	加速度/(m/s²)	变加速度/(m/s³)	角速度/(rad/s)	微动周期/s	采样点数
匀加速	[-2, 10]	[-5, 5]	-	-	-	2¹⁰Hz, 1 s
非匀变速	[-5, 15]	[-5, 5]	[-8, 8]	-	-	2¹¹ Hz, 0.5 s
微动Ⅰ	-	-	-	ω_x=[0.3, 0.38] ω_y=[0.1, 0.15] ω_z=[0.06, 0.08]	T_x=26.4 T_y=11.2 T_z=33.2	2¹¹ Hz, 0.5 s
微动Ⅱ	-	-	-	ω_x=[0.5, 0.58] ω_y=[0.8, 0.95] ω_z=[0.54, 0.58]	T_x=12.2 T_y=6.8 T_z=14.2	2¹¹Hz, 0.5 s

参数	取值
距离分辨力/m	0.5
观测时间/s	1
采样点数/个	10 240
叶片长度和宽度/m	L=6, W=1
旋转速率/(rad/s)	4

模型	分类准确率/%	测试用时/s
TL-CBAM-Swin-Transformer	98.63	5.94
Swin-Transformer	95.20	14.60
LeNet	93.43	6.50
AlexNet	82.16	8.92
GoogLeNet	89.60	10.23

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Classification of maritime micromotion target based on transfer learning in CBAM-Swin-Transformer

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