Decoupled distributed Kalman fuser for descriptor systems

Linear discrete stochastic descriptor systems with multisensor have been transformed, using the singular value decomposition (SVD), into two reduced-order non-descriptor subsystems with multisensor. Based on the linear minimum variance optimal fusion rule weighted by diagonal matrices, a decoupled distributed Kalman fuser is presented by using the Kalman filtering method and white noise estimation theory. With this procedure it is possible to handle the fused filtering, smoothing, and prediction problems in a unified framework, and realize a decoupled fused estimation for state components. Its accuracy is higher than that of each local Kalman estimator. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors have been presented. A Monte Carlo simulation example shows its effectiveness.