基于 TimeGAN-LSTM 的无人机 GPS欺骗干扰检测模型

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。

UAV GPS spoofing detection model based on TimeGAN-LSTM

To address the problem that unmanned aerial vehicle (UAV) is vulnerable to GPS spoofing, an UAV GPS spoofing detectionmodel based on long short-term memory ( LSTM) is proposed. In order to improve the training accuracy of the model, the trainingdataset was firstly enhanced using time series generative adversarial networks (TimeGAN) to compensate for the lack of training data andto compare the performance difference between the enhanced dataset and the original dataset. The LSTM model was then built, andexperimental results show that the accuracy, precision, recall and F1 value trained by the TimeGAN+LSTM model under simulationexperiments are 98. 08%, 98. 55%, 98. 07% and 98. 31%. Finally, the comparison with the traditional machine learning model provesthat the proposed spoofing detection model has better performance metrics. The model can achieve effective detection of UAV GPSspoofing signals.