To operate, autonomous systems of necessity employ a variety of sensors to perceive their environment. Many small unmanned aerial vehicles (UAV) are unable to carry redundant sensors due to size, weight, and power (SWaP) constraints. Faults in these sensors can cause undesired behavior, including system instability. Thus, detection of faults in these non-redundant sensors is of paramount importance.The problem of detecting sensor faults in non-redundant sensors on board autonomous aircraft is non-trivial. Factors that make development of a solution difficult include both an inability to perfectly characterize systems and sensors as well as the SWaP constraints inherent with small UAV. An additional challenge is the ability of a fault-detection method to strike a balance between false-alarm rate and detection rate.This thesis explores two model-based methods of fault-detection for non-redundant sensors, a Kalman filter based method and a particle filter based method. The Kalman filter based method employs tests of mean and covariance on the normalized innovation sequence to detect faults, while the particle filter based method uses a function of the average particle weights.The Kalman filter based approach was implemented in real time on board an autonomous rotorcraft using an extended Kalman Filter (EKF). Faults tested included varied levels of bias, drift, and increased noise. Metrics included false-alarm rate, detection rate, and delay to detection. The particle filter based approach was implemented on a simulated system. This was then compared with an implementation of the EKF based approach for the same system. The same fault types and metrics were also used for these tests.The EKF based method of fault-detection performed well onboard the autonomous rotorcraft and should be generalizable to other systems for which an EKF or Kalman filter can be implemented. The theory indicates that the particle filter based algorithm should have performed better, though the simulations showed poor detection characteristics in comparison to the Kalman filter based method. Future work should be performed to explore improvements to the particle filter based method.