一种基于LSTM的卫星导航欺骗干扰检测方法

赵慎; 黄文娜; 覃业梅; 廖一霏; 杨玲玲

doi:10.11959/j.issn.2096-6652.202517

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一种基于LSTM的卫星导航欺骗干扰检测方法

学术论文 | 更新时间：2025-07-21

- 一种基于LSTM的卫星导航欺骗干扰检测方法
- A satellite navigation spoofing interference detection method based on LSTM
- 智能科学与技术学报 2025年7卷第2期页码：234-245
- 作者机构：
  
  1.湖南工商大学智能工程与智能制造学院，湖南长沙 410205
  2.湘江实验室，湖南长沙 410205
- 作者简介：
  
  [ "赵慎（1983- ），男，博士，湖南工商大学智能工程与智能制造学院副教授，主要研究方向为导航时空信息安全、智能无线感知。" ]
  [ "黄文娜（2001- ），女，湖南工商大学智能工程与智能制造学院硕士生，主要研究方向为导航时空信息安全。" ]
  [ "覃业梅（1979- ），女，博士，湖南工商大学智能工程与智能制造学院教授，主要研究方向为时间序列信号分析与预测。" ]
  [ "廖一霏（2000- ），女，湖南工商大学智能工程与智能制造学院硕士生，主要研究方向为导航时空信息安全。" ]
  [ "杨玲玲（1993- ），女，博士，湖南工商大学智能工程与智能制造学院讲师，主要研究方向为中红外激光、光电信号检测及处理。" ]
- 基金信息：
  
  国家自然科学基金项目(61976088);湖南省重点研发计划项目(2024AQ2033);湖南省自然科学基金项目(2024JJ9188;2025JJ90243);湖南省教育厅科研基金项目(21B0577)
- DOI：10.11959/j.issn.2096-6652.202517
  中图分类号： TN96;TP391
- 收稿日期：2025-01-21，
  
  修回日期：2025-04-11，
  
  纸质出版日期：2025-06-15
- 稿件说明：
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赵慎,黄文娜,覃业梅等.一种基于LSTM的卫星导航欺骗干扰检测方法[J].智能科学与技术学报,2025,07(02):234-245.

ZHAO Shen,HUANG Wenna,QIN Yemei,et al.A satellite navigation spoofing interference detection method based on LSTM[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):234-245.
赵慎,黄文娜,覃业梅等.一种基于LSTM的卫星导航欺骗干扰检测方法[J].智能科学与技术学报,2025,07(02):234-245. DOI： 10.11959/j.issn.2096-6652.202517.

ZHAO Shen,HUANG Wenna,QIN Yemei,et al.A satellite navigation spoofing interference detection method based on LSTM[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):234-245. DOI： 10.11959/j.issn.2096-6652.202517.

摘要

针对卫星导航欺骗干扰检测问题，提出一种基于LSTM的卫星导航欺骗干扰检测方法。通过分析欺骗干扰对接收机环路的影响，选取跟踪环路中的直接与间接信息作为欺骗检测特征参数；采用滑动窗口对特征参数进行预处理，并按时间序列构建训练集和测试集，以保持特征的时间关联性。基于得克萨斯大学奥斯汀分校公开的TEXBAT数据集进行实验，实验结果表明，相较于现有SVM方法，所提方法将欺骗检测准确率提升了2.19%，欺骗场景识别准确率达99.98%，能有效解决导航欺骗检测和场景识别问题。

Abstract

Facing the problem of satellite navigation spoofing interference detection

a satellite navigation spoofing interference detection method based on LSTM was proposed. Based on the analysis of the effect of spoofing interference on the receiver loop

the direct and indirect information in the tracking loop was selected as the spoofing detection feature parameters. The feature parameters were preprocessed using the sliding window

and the training and test sets were constructed according to the time series to ensure the temporal relevance of the features. Based on the experiments conducted on the TEXBAT dataset from the university of Texas at Austin

this paper’s method can improve the accuracy of spoofing detection by 2.19% and spoofing scene recognition by 99.98% compared with the existing SVM methods

which can effectively solve the problem of navigation spoofing detection and scene recognition.

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Keywords

references

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一种基于LSTM的卫星导航欺骗干扰检测方法