小样本下雷达复合干扰半监督迁移学习识别方法

doi:10.11959/j.issn.1000-0801.2023182

Abstract

Abstract:

Aiming at the problem that more and more kinds of radar compound jamming signals and too few training samples were difficult to make the deep learning model reach the optimal state, a semi-supervised transfer learning recognition method for radar compound jamming under small samples was proposed, which solved the problem of low network training accuracy caused by the difficulty in obtaining labeled samples through unlabeled samples.The feature extractor and classifier obtained after pre-training of single jamming data set were transferred to small-scale compound jamming data set, and the model was fine-tuning by using weight imprinting and semi-supervised learning.The model parameters were optimized by the proposed nearest neighbor correlation loss nearest neighbor correlation loss (NNCL).The experimental results show that the recognition accuracy of the model can reach 93.20% when the jamming-to-noise ratio is 10 dB and there are only 5 labeled samples of the new class of compound jamming signals.

Key words: radar anti-jamming, compound jamming recognition, transfer learning, weight imprinting, semi-supervised learning

CLC Number:

TN975

Jinqiang WANG, Minhong SUN, Xianghong TANG, Zhaoyang QIU, Deguo ZENG. A semi-supervised transfer learning recognition method for radar compound jamming under small samples[J]. Telecommunications Science, 2023, 39(10): 15-28.

Figures/Tables 14

层名	输出维度	ResNet块类型B(3,3)
conv1	32×32	$[3 \times 3 ， 16]$
conv2	32×32	$[\begin{matrix} 3 \times 3 ， 16 \times 10 \\ 3 \times 3 ， 16 \times 10 \end{matrix}] \times 3$
conv3	16×16	$[\begin{matrix} 3 \times 3 ， 32 \times 10 \\ 3 \times 3 ， 32 \times 10 \end{matrix}] \times 4$
conv4	8×8	$[\begin{matrix} 3 \times 3 ， 64 \times 10 \\ 3 \times 3 ， 64 \times 10 \end{matrix}] \times 6$
avg-pool	1×1	$[8 \times 8]$

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信号参数	参数值
调制类型	LFM
脉宽/μs	20
采样频率/MHz	40
RD时延/μs	2～4
SMSP采样倍数	3～5
AMN/FMN带宽/MHz	20～40
载频/MHz	20
信号带宽/MHz	10
脉冲重复间隔/μs	100
欺骗速度/(m·s^-1)	100～300
COMB谱个数	4～6
FMN调频斜率/(MHz·V^-1)	2

未带标签样本数	5-way 1-shot	5-way 5-shot
5	74.21%±0.77%	82.15%±0.86%
10	77.82%±0.75%	85.69%±0.78%
15	80.12%±0.61%	88.51%±0.72%
20	81.56%±0.46%	88.74%±0.53%
30	81.32%±0.39%	89.06%±0.32%

复合干扰类型		JNR比值
复合干扰类型	1:1	1:5	1:10
RD+AMN	92.62%±0.51%	88.63%±0.61%	81.55%±0.32%
RD+FMN	92.07%±0.16%	88.33%±0.56%	81.16%±0.82%
SMSP+AMN	93.14%±0.35%	89.20%±0.24%	82.87%±0.48%
SMSP+FMN	93.67%±0.64%	89.52%±0.37%	82.64%±0.94%
R-VD+AMN	92.85%±0.72%	87.79%±0.58%	80.86%±0.16%
R-VD+FMN	92.04%±0.64%	87.06%±0.35%	81.21%±0.42%

方法	5-way 1-shot	5-way 5-shot
WI^[14]	69.84%±0.86%	78.92%±0.62%
MixMatch^[16]	64.62%±0.77%	81.65%±0.74%
MixMatch+NNCL	71.68%±0.79%	88.36%±0.48%
WI+MixMatch	77.63%±0.91%	89.86%±0.42%
WI+MixMatch+NNCL	84.31%±0.68%	93.20%±0.55%

算法模型	特征提取网络	5-way 1-shot	5-way 5-shot
Fine-tuning^[21]	ResNet 34	54.41%±0.89%	63.09%±0.72%
	WRN-28-10	58.63%±0.75%	66.21%±0.69%
Pseudo-Labeling^[22]	ResNet 34	73.33%±0.96%	84.30%±0.91%
	WRN-28-10	75.16%±0.63%	85.21%±0.80%
Baseline++^[23]	ResNet 34	67.54%±0.82%	74.68%±0.89%
	WRN-28-10	69.66%±0.74%	75.54%±0.52%
本文算法	ResNet 34	82.75%±0.71%	91.02%±0.65%
	WRN-28-10	84.31%±0.68%	93.20%±0.55%

A semi-supervised transfer learning recognition method for radar compound jamming under small samples

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Figures/Tables 14

References 23

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算法模型	单次训练平均时间/s	单次识别平均时间/s
Fine-tuning^[21]	0.330	0.208
Pseudo-Labeling^[22]	0.289	0.126
Baseline++^[23]	0.355	0.061
本文算法	0.409	0.059

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