A modeling approach to state estimation in systems with switching parameters

A novel approach to state estimation in linear, discrete-time systems which switch randomly between two sets of parameters is described. A nonlinear approximate_ model for the switching process is developed and used for the joint estimation of the state vector and a switching variable, via a single extended Kalman filter. The performance of the filter is compared with that of other Suboptimal filters known to date. The proposed approach provides comparable accuracy, and saves up to 50 percent of computational effort in systems of moderate or large size.