Inverse Covariance Intersection Fusion Steady-state Kalman filter for Uncertain Systems with Multiplicative Noises, Missing Measurements and Linearly Correlated White Noises

This paper is concerned with inverse covariance intersection fusion problem of uncertain linear systems with multiplicative noises, missing measurements and uncertain linearly correlated white noises. By introducing the fictitious noises to compensate the stochastic uncertainties, the system under consideration can be converted into one with only uncertain noise variances. The steady-state Kalman filter is designed by inverse covariance intersection (ICI) fuser. It overcomes the disadvantage that the covariance intersection (CI) fuser has larger conservativeness. The accuracy of the ICI filter is higher than CI filter and that of local filter. A simulation example is given to verify the accuracy relations.