Innovation sequence application to aircraft sensor fault detection: comparison of checking covariance matrix algorithms

In this paper, the algorithms verifying the covariance matrix of the Kalman filter innovation sequence are compared with respect to detected minimum fault rate and detection time. Four algorithms are dealt with; the algorithm verifying the trace of the covariance matrix of the innovation sequence, the algorithm verifying the sum of all elements of the inverse covariance matrix of the innovation sequence, the optimal algorithm verifying the ratio of two quadratic forms of which matrices are theoretic and selected covariance matrices of Kalman filter innovation sequence, and the algorithm verifying the generalized variance of the covariance matrix of the innovation sequence. The algorithms are implemented for longitudinal dynamics of an aircraft to detect sensor faults, and some suggestions are given on the use of the algorithms in flight control systems.