Fault detection in flight control systems via innovation sequence of Kalman filter

In this paper, a real-rime approach to detect the faults affecting the covariance matrix of the Kalman filter innovation sequence is presented. The ratio of two quadratic forms of which matrices are theoretic 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 faults in the sensors rapidly. The longitudinal dynamics of an aircraft control system, as an example, is considered, and detection of various sensor faults affecting the mean and covariance matrix is examined.