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

For systems with missing measurements and linearly correlated white noises, by introducing the fictitious noises to compensate the multiplicative noise terms, the original system can be converted into one with only noise variances. The steady-state Kalman filter is designed by inverse covariance intersection (ICI) fusion. 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.