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

被引：0

作者：

Deng Z. ^{[1
]}

Lei J. ^{[1
]}

Sun C. ^{[1
]}

机构：

[1] College of Electronic Science, National University of Defense Technology, Changsha

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 07期

关键词：

attention mechanism; frequency hopping detection; interference cancelation; semisupervised learning;

D O I：

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

中图分类号：

学科分类号：

摘要：

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. © 2023 Chinese Institute of Electronics. All rights reserved.

引用

页码：2236 / 2248

页数：12

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