深度置信网络在四旋翼无人机传感器攻击检测中的应用OA

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.