现代电子技术2026，Vol.49Issue(7)：26-30,39,6.DOI:10.16652/j.issn.1004-373x.2026.07.005
利用CNN-LSTM融合模型实现GNSS诱导式欺骗干扰检测
GNSS induced spoofing jamming detection using CNN-LSTM fusion model
孙明哲 1王振岭 1郝放2
作者信息
- 1. 中国电子科技集团公司第五十四研究所,河北 石家庄 050081
- 2. 中国电子科技集团公司第五十四研究所,河北 石家庄 050081||东南大学 仪器科学与工程学院,江苏 南京 210096
- 折叠
摘要
Abstract
Satellite navigation receivers have limited capabilities in countering induced spoofing attacks,and the traditional detection methods used are faced with challenges such as real-time processing difficulties and poor adaptability to preset discrimination thresholds.In view of this,the paper proposes a fusion neural network detection method based on CNN-LSTM.Firstly,the correlation peak aliasing characteristics during the spoofing pull-off phase was analyzed.Then,the ResNet-18 was used as the backbone of the convolutional neural network(CNN)to extract spatial features in the code phase domain and Doppler domain,and the long short-term memory(LSTM)network was employed to track the temporal dependencies across consecutive frames,so as to detect the inducing behavior of deceptive signals.To simulate the induced spoofing process,a correlation ambiguity function(CAF)sequence dataset was constructed to verify the detection performance of the fusion model.Experiments show that the detection accuracy rate of the proposed method for induced spoofing attacks exceeds 98%,which is improved by 2%than that of the traditional single models.Moreover,both the detection duration and model complexity can meet the requirements of civilian receivers.To sum up,it is an effective method in the field of anti-spoofing application of satellite navigation.关键词
欺骗干扰检测/卫星导航/诱导式欺骗/卷积神经网络/长短期记忆网络/残差网络Key words
spoofing jamming detection/GNSS/induced spoofing/CNN/LSTM network/residual network分类
信息技术与安全科学引用本文复制引用
孙明哲,王振岭,郝放..利用CNN-LSTM融合模型实现GNSS诱导式欺骗干扰检测[J].现代电子技术,2026,49(7):26-30,39,6.
