Weighted Fusion Incremental Kalman Estimator for Descriptor Systems under Poor Observation Condition

Under certain environmental conditions, the unknown system errors often occur and yield to larger filtering errors when the unverified or uncalibrated measurement equation is used. Incremental equation can be introduced, which will effectively solve the problem of state estimation for the systems under poor observation condition. For the linear discrete descriptor systems under poor observation condition, a descriptor incremental Kalman estimation algorithm is firstly presented under the canonical form. It solves the state estimation problem for the descriptor systems with unknown measurement errors, which doesn't meet the requirements of classical Kalman filter. Furthermore, an optimal weighted fusion descriptor incremental Kalman estimator is given based on the linear minimum variance optimal fusion criterion. Compared with the existing augmented dimension-enlarged processing methods, the proposed algorithms are simple in form and small in computational burden so as to be easily applied in engineering practice. A simulation example shows its effectiveness and feasibility.