跳频信号盲检测的半监督干扰对消方法

doi:10.12305/j.issn.1001-506X.2023.07.35

Abstract

Abstract:

The real electromagnetic environment for real hopping frequency is complex and unpredictable, which poses a problem to the detection algorithm based on simulation data training. To address this problem, a method called semi-supervised interference cancellation is proposed. The method firstly introduces a graph attention mechanism and an ensemble channel attention module with Siamese nested Unet backbone to obtain an interference cancellation network, and pretrains it with paired spectrograms of hopping signals and corresponding labels to obtain the ability of interference cancellation and signal detection. Secondly, input the unlabeled spectrograms with more complex interference to the interference cancellation network to obtain low-entropy predictions as pseudo labels. Meanwhile, the unlabeled spectrograms are also strongly enhanced to obtain the distorted spectrograms. The network is trained so that the detection results of the distorted spectrograms are consistent with the pseudo-label, thus strengthening the generalization ability of the network on the unlabeled data. The simulation results show that the proposed method can achieve parameter estimation and blind detection under complex interference and enhance the network performance with unlabeled data.

Key words: frequency hopping detection, interference cancelation, attention mechanism, semi-supervised learning

CLC Number:

TN911.7

Zhe DENG, Jing LEI, Chengzhe SUN. Semi-supervised interference cancellation method for frequency hopping signal blind detection[J]. Systems Engineering and Electronics, 2023, 45(7): 2236-2248.

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References 26

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名称	描述	幅度
翻转	将图像水平或者垂直对调	-
调换	将两个输入调换位置	-
平移/%	将图像沿着某轴平移	[-0.2, 0.2]
遮挡/%	用方块遮挡住图像的一部分	[0.2, 0.4]
裁剪/%	沿着某轴裁剪图像	[-0.2, 0.2]

参数	取值
有标签数据总数/对	400
测试数据集大小/对	1 452
有标签数据信噪比/dB	0~10
有标签数据信干比/dB	0~10
无标签、测试数据信噪比/dB	-10~0
无标签、测试数据信干比/dB	-10~0
跳频频率集大小/点	64
符号速率/(sym/s)	5×10⁵
采样率/Hz	6.144×10⁷
信号帧长/s	4×10^-2
最大多普勒频移/Hz	100
多径数量	3
莱斯信道K因子	4
多径延迟/s	[0 0.9 1.7]×10^-5
短时傅里叶变换窗	256点凯撒窗
时频图大小/点	[256 256]

参数	取值
优化器	AdamW
初始学习率	1×10^-3
学习率衰减	1×10^-2
训练轮次	100.0
无监督损失加权系数w	2.0
预测熵门限值λ_e	0.3

网络	F1	IoU	漏警率	虚警率
CA-CFAR	—	—	0.199 7	0.067 6
DeepLabv3+	0.735 0	0.593 0	0.030 1	0.033 7
UNet	0.653 0	0.617 0	0.026 4	0.014 5
ICnet	0.969 0	0.891 0	0.056 7	4.64×10^-4

比例	F1	交并比	漏警率	虚警率
全监督	0.951 0	0.841 0	0.107 8	7.258×10^-4
1∶1	0.956 0	0.856 0	0.085 6	4.734×10^-4
1∶2	0.967 0	0.889 0	0.058 6	4.821×10^-4
1∶4	0.969 0	0.891 0	0.056 7	4.64×10^-4

Semi-supervised interference cancellation method for frequency hopping signal blind detection

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团数	F1	IoU	漏警率	虚警率
3	0.969	0.891	0.056 7	4.64×10^-4
4	0.975	0.895	0.061 4	1.59×10^-4
5	0.976	0.894	0.063 4	1.21×10^-4

网络	浮点运算次数/G	总参数量/M
SqueezeNet	0.700	1.250
TFCnet-noGT	4.673	1.606
TFCnet-noCA	4.661	1.131
TFCnet-noECAM	4.675	1.620
TFCnet	4.680	1.621

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