Multisensor Data-Fusion-Based Approach to Airspeed Measurement Fault Detection for Unmanned Aerial Vehicles

Fault detection (FD) plays an important role in guaranteeing system safety and reliability for unmanned aerial vehicles (UAVs). This paper focuses on developing an alternative approach to FD for airspeed sensor in UAVs by using data from gyros, accelerometers, global positioning system, and wind vanes. Based on the kinematics model of the UAV, an estimator is proposed to provide analytical redundancy using information from the above-mentioned sensors, which are commonly implemented on UAVs. This filter process is independent of the airspeed measurement and the aircraft dynamics model. Furthermore, we employ the observability rank criterion based on Lie derivatives and prove that the nonlinear system describing the airspeed kinematics is observable. The χ2 test and cumulative sum detector are employed to detect the occurrence of airspeed measurement faults together. Finally, the performance of the proposed methodology has been evaluated through flight experiments of UAVs.