| 王路阳,孙一宸,于明鑫,李天放,董明利.基于 TimeGAN-LSTM 的无人机 GPS
欺骗干扰检测模型[J].电子测量与仪器学报,2023,37(6):122-135 |
| 基于 TimeGAN-LSTM 的无人机 GPS
欺骗干扰检测模型 |
| UAV GPS spoofing detection model based on TimeGAN-LSTM |
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| DOI： |
| 中文关键词: 无人机 GPS 欺骗干扰检测 深度学习 TimeGAN LSTM |
| 英文关键词:UAV GPS spoofing detection deep learning TimeGAN LSTM |
| 基金项目:北 京 市 教 委 科 技 计 划 一 般 项 目 ( KM202011232007 )、 高 校 学 科 人 才 引 进 计 划 ( D17021 )、 北 京 信 息 科 技 内 涵 发 展 项 目(2019KYNH204)资助 |
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| 中文摘要: |
| 针对无人机易受 GPS 欺骗干扰的问题,提出一种基于长短时记忆法(LSTM)的无人机全球定位系统(GPS)欺骗干扰检
测模型。 为了提高模型训练精度,首先利用时序生成对抗网络(TimeGAN)对训练数据集进行了数据增强工作,弥补了训练数
据量的不足,还对比了增强数据集与原始数据集的性能差距。 然后搭建了 LSTM 模型,在仿真实验下 TimeGAN+LSTM 模型获
得的准确率、精确率、召回率和 F1 值分别为 98. 08%、98. 55%、98. 07% 和 98. 31%。 最后与传统机器学习模型进行比较,对比
结果证明,提出的欺骗干扰检测模型拥有更好的性能指标。 该模型可实现对无人机 GPS 欺骗干扰信号的有效检测。 |
| 英文摘要: |
| To address the problem that unmanned aerial vehicle (UAV) is vulnerable to GPS spoofing, an UAV GPS spoofing detection
model based on long short-term memory ( LSTM) is proposed. In order to improve the training accuracy of the model, the training
dataset was firstly enhanced using time series generative adversarial networks (TimeGAN) to compensate for the lack of training data and
to compare the performance difference between the enhanced dataset and the original dataset. The LSTM model was then built, and
experimental results show that the accuracy, precision, recall and F1 value trained by the TimeGAN+LSTM model under simulation
experiments are 98. 08%, 98. 55%, 98. 07% and 98. 31%. Finally, the comparison with the traditional machine learning model proves
that the proposed spoofing detection model has better performance metrics. The model can achieve effective detection of UAV GPS
spoofing signals. |
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