控制理论与应用2026，Vol.43Issue(4)：774-782,9.DOI:10.7641/CTA.2024.40199
深度置信网络在四旋翼无人机传感器攻击检测中的应用
Application of deep belief network in sensor attack detection for quadrotor UAVs
摘要
Abstract
To achieve fast and accurate detection of sensor attacks on quadrotor UAVs,this paper proposes an attack detection algorithm based on state estimation and deep learning.Firstly,the algorithm uses the extended Kalman filter(EKF)to estimate the UAV's state and extract feature information from sensor measurements.Then,a sliding temporal window is applied to construct detection information,and a deep belief network(DBN)is used to establish a nonlinear mapping between the detection information and the sensor state(whether under attack).EKF simplifies the acquisition of sensor state detection information,while DBN accurately fits the complex nonlinear relationship,significantly improving detection accuracy.Furthermore,an adaptive EKF algorithm is designed to dynamically adjust the measurement noise covariance matrix upon detecting a sensor attack,enhancing the reliability of state estimation.Simulation results show that the proposed EKF-DBN detection algorithm outperforms traditional methods in terms of accuracy and detection efficiency.关键词
扩展卡尔曼滤波器/深度置信网络/攻击检测/四旋翼无人机/自适应滤波Key words
quadrotor UAV/extended Kalman filter/deep belief network/attack detection/adaptive filtering引用本文复制引用
石鹏程,赵振根,李庆龙..深度置信网络在四旋翼无人机传感器攻击检测中的应用[J].控制理论与应用,2026,43(4):774-782,9.基金项目
国家自然科学基金基础科学中心项目(62388101),国家自然科学基金面上项目(62473195),国家自然科学基金重点项目(62233009),中国博士后科学基金面上项目(2021M701701),中央高校基本科研业务费专项资金项目(NS2024017)资助. Supported by the Basic Science Center Program of the National Natural Science Foundation of China(62388101),the National Natural Science Foundation of China General Program(62473195),the National Natural Science Foundation of China Key Program(62233009),the China Postdoc-toral Science Foundation(2021M701701)and the Fundamental Research Funds for the Central Universities(NS2024017). （62388101）
