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.