基于LSTM-KF模型的无人机抗GPS欺骗方法

孙旸; 曹春杰; 赖俊晓; 于天娇

doi:10.11959/j.issn.2096-109x.2020069

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基于LSTM-KF模型的无人机抗GPS欺骗方法

学术论文 | 更新时间：2024-06-03

- 基于LSTM-KF模型的无人机抗GPS欺骗方法
- AntiGPS spoofing method for UAV based on LSTM-KF model
- 网络与信息安全学报 2020年6卷第5期页码：80-88
- 作者机构：
  
  1. 海南省Internet信息检索重点实验室，海南海口 570228
  2. 海南大学计算机与网络空间安全学院，海南海口 570228
  3. 南海海洋资源利用国家重点实验室，海南海口570228
- 作者简介：
  
  [ "孙旸（1994- ），男，山西大同人，海南大学硕士生，主要研究方向为无人机安全、物联网安全" ]
  [ "曹春杰（1977- ），男，河北衡水人，海南大学教授、博士生导师，主要研究方向为无线网络安全、无人机安全、信息对抗、区块链安全" ]
  [ "赖俊晓（1994- ），男，福建漳州人，海南大学硕士生，主要研究方向为人工智能、大数据和物联网安全" ]
  [ "于天娇（1995- ），女，河南安阳人，海南大学硕士生，主要研究方向为物联网安全、无人机安全和区块链安全" ]
- 基金信息：
  
  国家自然科学基金;The National Natural Science Foundation of China(61661019);国家自然科学基金联合基金重点项目;The State Key Program of National Natural Science Foundation of China(U19B2044);海南省高等学校教育教学改革研究重点资助项目;Key Project of Education Teaching Reform Research in Higher Education Institutions in Hainan Province(Hnjg2017ZD-1)
- DOI：10.11959/j.issn.2096-109x.2020069
  中图分类号： TP393
- 网络首发：2020-10，
  
  纸质出版：2020-10-15
- 稿件说明：
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孙旸, 曹春杰, 赖俊晓, 等. 基于LSTM-KF模型的无人机抗GPS欺骗方法[J]. 网络与信息安全学报, 2020,6(5):80-88.

Yang SUN, Chunjie CAO, Junxiao LAI, et al. AntiGPS spoofing method for UAV based on LSTM-KF model[J]. Chinese Journal of Network and Information Security, 2020, 6(5): 80-88.
孙旸, 曹春杰, 赖俊晓, 等. 基于LSTM-KF模型的无人机抗GPS欺骗方法[J]. 网络与信息安全学报, 2020,6(5):80-88. DOI： 10.11959/j.issn.2096-109x.2020069.

Yang SUN, Chunjie CAO, Junxiao LAI, et al. AntiGPS spoofing method for UAV based on LSTM-KF model[J]. Chinese Journal of Network and Information Security, 2020, 6(5): 80-88. DOI： 10.11959/j.issn.2096-109x.2020069.

摘要

针对无人机 GPS 信号易受干扰、易欺骗的问题，提出一种结合深度学习和卡尔曼滤波的无人机抗GPS欺骗的检测方法，该方法通过使用长短期记忆网络从无人机飞行状态中预测得到无人机飞行的动力学模型，并利用卡尔曼滤波结合动力学模型进行动态调整来识别GPS欺骗，从而达到抵御GPS欺骗信号干扰的目的，同时该方法无须增加接收器的硬件开销，且易于实现。实验结果证明，相比同类方法，该方法对于识别GPS信号具有更高的准确率和更低的误报率，可有效增强无人机抗GPS欺骗干扰的能力。

Abstract

A detection method of anti GPS deception of UAV was proposed for the problem that GPS signal of UAV was easy to be interfered and deceived

which combined deep learning and Kalman filter.The dynamic model of UAV flight was predicted from the flight state of UAV by using long short-term memory network

and the dynamic adjustment of Kalman filter and dynamic model was used to identify GPS deception.In order to resist the interference of GPS deception signal

this method did not need to increase the hardware overhead of the receiver

and was easy to realize.The experimental results show that the method has higher accuracy and lower false alarm rate for the recognition of GPS signals

and can effectively enhance the UAV's ability to resist GPS deception interference.

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Keywords

references

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