A sensor fault detection for aircraft using a single Kalman filter and hidden Markov models
Konrad Rudin, Guillaume Ducard, Roland Siegwart · 2014
This paper presents a new scheme for sensor fault detection and isolation. It uses a single Kalman filter and a Gaussian hidden Markov model for each of the monitored sensors. This combination is able to simultaneously detect single and multiple sensor faults, still guaranteeing optimal system state estimation. This algorithm also can run on systems with limited computational power. The efficiency of the approach is evaluated through simulation of an aircraft to detect airspeed and GPS sensor faults. The results show fast fault detection and low false-alarm rate.