Fault tolerant estimation of F-16 fighter dynamics against sensor faults

Abstract

In this study the discrete nonlinear model of the F-16's flight dynamics and the estimation in real time of the aircraft states using extended Kalman filtering technique are investigated. The main topics of this study are fault tolerant estimation systems, which include the study of the robust Kalman filter, and the reconfigurable Kalman filter. Two different robust Kalman filter algorithms are studied: the first one based on single measurement noise scale factor and the second one based on multiple measurement noise scale factor. Finally, with a reconfigurable Kalman filter, when a fault is detected, the faulty sensor can be switched off. Doing that, the system model changes and is less reliable because the error of the estimations increases, but as long as the system fulfils the initial requirements it still can be used. The algorithms investigated have been applied to the F-16's nonlinear dynamic model, and the estimated values fit the theory.