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

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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：2025-05-07

- 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
- 作者机构：
  
  1.湘江实验室,湖南省长沙市410205
  2.湖南工商大学智能工程与智能制造学院,湖南省长沙市410205
- 作者简介：
- 基金信息：
  
  国家自然科学基金(61976088┫项目);湖南省重点研发计划项目(2024AQ2033);湖南省自然科学基金(2024JJ9188);项目湖南省教育厅科研基金(21B0577┫项目)
- DOI：
  中图分类号： TN96;TP391
- 收稿日期：2025-01-20，
  
  修回日期：2025-04-16，
  
  录用日期：2025-04-28，
- 稿件说明：
移动端阅览
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/OL]. 智能科学与技术学报, 2025.

ZHAO Shen, HUANG Wenna, QIN Yemei, et al. A Satellite Navigation Spoofing Interference Detection Method Based on LSTM[J/OL]. Chinese journal of intelligent science and technology, 2025.
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/OL]. 智能科学与技术学报, 2025. DOI：

ZHAO Shen, HUANG Wenna, QIN Yemei, et al. A Satellite Navigation Spoofing Interference Detection Method Based on LSTM[J/OL]. Chinese journal of intelligent science and technology, 2025. DOI：

摘要

面向卫星导航欺骗干扰检测的问题，本文提出一种适用于长短期记忆神经网络（Long Short-term Memory，LSTM）检测欺骗干扰的特征参数选取及数据处理方法。基于欺骗干扰对接收机环路的影响分析，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；利用滑窗技术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，以保证特征的时间关联性。基于美国德克萨斯大学奥斯汀分校的TEXBAT数据集进行实验，相较于现有SVM方法，本文方法可将欺骗检测的准确率提高2.19%，且欺骗场景识别的准确率达99.98%，可有效解决导航欺骗的检测和场景识别问题。

Abstract

Facing the problem of satellite navigation spoofing interference detection

this paper proposes a feature parameter selection and data processing method applicable to the detection of spoofing interference by Long Short-term Memory (LSTM) neural network. Based on the analysis of the effect of spoofing interference on the receiver loop

the direct and indirect information in the tracking loop is selected as the spoofing detection feature parameters; the feature parameters are preprocessed using the sliding window technique

and the training and test sets are 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 to the existing SVM methods

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

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references

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