轻量化的ML-SNet雷达复合干扰识别算法

doi:10.12305/j.issn.1001-506X.2025.02.09

Abstract

Abstract:

Aiming at the problems of difficult radar compound jamming recognition and high complexity of network model in complex electromagnetic environments, a lightweight multi-labeling ShuffleNet (ML-SNet) radar compound jamming recognition algorithm is proposed by combining multi-label classification with the improved ShuffleNet V2. Firstly, the lightweight ShuffleNet V2 is used as the backbone network, and the similarity-based attention module (SimAM) attention mechanism is introduced to improve the network feature extraction capability. Secondly, the funnel activation rectified linear unit (FReLU) activation function is used instead of the rectified linear unit (ReLU) activation function to reduce the information loss of the feature map. Finally, the recognition results are obtained by classifying the network output using a multi-label classification algorithm. Experimental results indicate that the proposed algorithm achieves an average recognition rate of 97.9% for 15 classes of radar compound jamming with the jamming-to-noise ratio of -10-10 dB. The proposed algorithm has a lower computational complexity and the best performance in terms of recognition performance compared to other networks.

Key words: compound jamming recognition, multi-label classification, lightweight, computational complexity

CLC Number:

TN974

Limin GUO, Wenqing HUANG, Qian CHEN, Jiabin WANG. Lightweight algorithm of ML-SNet radar compound jamming recognition[J]. Systems Engineering and Electronics, 2025, 47(2): 418-427.

Figures/Tables 12

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References 30

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干扰类型	参数设置	训练集	测试集
CI	切片宽度/μs	2	2~3
CI	重复转发次数/次	4	4~5
ISDJ	切片宽度/μs	2	2~2.5
ISDJ	切片次数/次	4	4~5
COMB	频移个数/个	5	4~5
SMSP	调频斜率	5k	4k~5k
SMSP	转发次数/次	5	4~5
NP	带宽/MHz	10~20	8~22
NC	带宽/MHz	10~20	8~22
复合干扰	与相应的干扰分量相同	与相应的干扰分量相同	与相应的干扰分量相同

模型	准确率/%	参数量(×10⁶)	运算量(×10⁹)
1	93.1	1.51	0.13
2	96.4	1.51	0.13
3	95.3	1.71	0.13
4	96.1	1.51	0.13
5	95.5	0.94	0.09
6	97.9	1.07	0.09

干扰类型	MobileNet V2	EfficientNet	ResNet34	ShuffleNet V2	文献[24]	ML-SNet
CI+ISDJ	97.7	95.5	100.0	92.0	98.9	95.9
CI+COMB	95.5	86.4	79.5	96.6	96.6	100.0
CI+SMSP	88.6	95.5	77.3	88.6	95.5	100.0
CI+NP	94.3	87.5	84.1	96.6	92.0	99.0
CI+NC	95.5	98.9	94.3	79.5	97.7	93.1
ISDJ+COMB	97.7	96.6	96.6	79.5	97.7	94.8
ISDJ+SMSP	95.5	92.0	97.7	97.7	98.9	98.9
ISDJ+NP	89.8	96.6	63.6	96.6	93.2	97.0
ISDJ+NC	84.1	89.8	61.4	97.9	96.6	98.9
COMB+SMSP	84.1	88.6	70.5	96.6	95.5	100.0
COMB+NP	97.7	98.9	95.5	100.0	98.9	100.0
COMB+NC	99.0	98.9	97.7	85.2	98.9	99.0
SMSP+NP	95.5	99.0	81.8	98.9	98.9	99.0
SMSP+NC	95.5	95.5	70.5	81.8	93.2	99.0
NP+NC	92.0	94.3	88.6	97.7	98.9	94.8

模型	空间复杂度(×10⁶)	时间复杂度
模型	空间复杂度(×10⁶)	每轮训练时间/s	平均识别时间/ms
ML-SNet	1.07	24.62	7.38
MobileNet V2	3.26	59.66	8.24
EfficientNet	4.12	103.17	9.41
ResNet34	36.80	52.63	8.88
ShuffleNet V2	1.51	33.87	7.47
文献[24]	5.33	113.26	9.92

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Lightweight algorithm of ML-SNet radar compound jamming recognition

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