Robust eigenvalue assignment for generalized systems
Automatica (Journal of IFAC)
A unified approach to optimal state estimation for stochastic singular systems
Automatica (Journal of IFAC)
Technical Communique: Descriptor Wiener state estimators
Automatica (Journal of IFAC)
New approach to information fusion steady-state Kalman filtering
Automatica (Journal of IFAC)
Multi-sensor optimal information fusion Kalman filter
Automatica (Journal of IFAC)
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
International Journal of Sensor Networks
Hi-index | 0.00 |
For the linear discrete stochastic descriptor systems with multisensor, using the singular value decomposition, it is transformed into two reduced-order non-descriptor subsystems. A new optimal fusion criterion weighted by the block-diagonal matrices is presented, and the corresponding steady-state descriptor Kalman fuser with a three-layer fusion structure is also presented by using the white noise estimation theory. It realizes decoupled fused estimation for two subsystems. In the linear minimum variance sense, the block-diagonal matrices are determined by three different rules such that the diagonal block matrices are matrices, diagonal matrices, or scalars, so that three optimal fused descriptor Kalman estimators weighted by the block-diagonal matrices are obtained. Their accuracy relations are proved. They can handle the fused filtering, smoothing, and prediction problems in a unified framework, and can improve the accuracy of local estimation. They are locally optimal, and are globally suboptimal. In order to compute the optimal weights, the formulas of computing the cross-covariance matrices among local estimation errors are presented. A Monte Carlo simulation example shows their effectiveness.