Abstract
When the measurement equation of the system is not verified or calibrated under certain environmental conditions, using the measurement equation will often produce unknown system error, which will lead to large filtering error. The introduction of the incremental equation can effectively solve the state estimation problem of this kind of system under poor observation condition. In this paper, a descriptor incremental Kalman estimation algorithm is proposed for the linear discrete singular systems under poor observation condition. The problem of state estimation for singular systems with unknown observation error is solved, which cannot be solved by classical Kalman filter. Furthermore, under the linear minimum variance optimal fusion criterion, an optimal weighted fusion descriptor incremental Kalman estimator is proposed. Compared with the existing methods, the algorithm proposed in this paper has the advantages of simple form, low computational burden and easy application in engineering. A simulation example proves its effectiveness and feasibility.
Published Version
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