Sensor fault detection in flight control systems based on the Kaiman filter innovation sequence

In this paper, a real-time approach to detect the faults affecting the mean and the covariance matrix of the Kaiman filter innovation sequence is presented. The ratio of two quadratic forms for which the matrices are theoretical and selected covariance matrices, as monitoring statistics, is used. The arguments of the optimal quadratic form that maximize the above statistics are determined to detect the faults in sensors rapidly. The longitudinal dynamics of an aircraft control system, as an example, is considered, and detection of the faults in pitch gyroscope affecting the mean and the covariance matrix is examined.