Abstract:In order to solve the problem that due to the lack of anomalous sensing data, it is difficult for the quadrotor unmanned aerial vehicle (UAV) to implement the motion condition assessment of flight control system, this paper proposes a method to generate anomalous attitude data based on physical simulation model. Firstly, the moving coordinate system of quadrotor unmanned aerial vehicle is defined and NewtonEulerian formula is used to establish the UAV motion equation. In this way, the control loop of the flight control system is designed. The physical simulation model of the flight control system is established with Simulink software, which provides the experiment environment for generating anomalous sensing data. Secondly, the real attitude sensing data of the quadrotor UAV are utilized to verify the applicability of the simulation model, and through abnormal injection the anomalous attitude data are generated. Finally, taking the anomaly detection method based on principle component analysis (PCA) as an example, the application effect of the generated anomalous sensing data is evaluated. Experiment results show that the method proposed in this paper can effectively generate two kinds of anomalous attitude sensing data with constant bias and drift. The anomaly detection results of PCA method show that false positive ratio is 2%～74% and accuracy is 73%～94%. Therefore, the proposed sensing data generation method can provide the corresponding data support for improving the performance of anomaly detection method.